Дисертації: "Lesion detection"

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Eltayef, Khalid Ahmad A. "Segmentation and lesion detection in dermoscopic images." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/16211.

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Malignant melanoma is one of the most fatal forms of skin cancer. It has also become increasingly common, especially among white-skinned people exposed to the sun. Early detection of melanoma is essential to raise survival rates, since its detection at an early stage can be helpful and curable. Working out the dermoscopic clinical features (pigment network and lesion borders) of melanoma is a vital step for dermatologists, who require an accurate method of reaching the correct clinical diagnosis, and ensure the right area receives the correct treatment. These structures are considered one of the main keys that refer to melanoma or non-melanoma disease. However, determining these clinical features can be a time-consuming, subjective (even for trained clinicians) and challenging task for several reasons: lesions vary considerably in size and colour, low contrast between an affected area and the surrounding healthy skin, especially in early stages, and the presence of several elements such as hair, reflections, oils and air bubbles on almost all images. This thesis aims to provide an accurate, robust and reliable automated dermoscopy image analysis technique, to facilitate the early detection of malignant melanoma disease. In particular, four innovative methods are proposed for region segmentation and classification, including two for pigmented region segmentation, one for pigment network detection, and one for lesion classification. In terms of boundary delineation, four pre-processing operations, including Gabor filter, image sharpening, Sobel filter and image inpainting methods are integrated in the segmentation approach to delete unwanted objects (noise), and enhance the appearance of the lesion boundaries in the image. The lesion border segmentation is performed using two alternative approaches. The Fuzzy C-means and the Markov Random Field approaches detect the lesion boundary by repeating the labeling of pixels in all clusters, as a first method. Whereas, the Particle Swarm Optimization with the Markov Random Field method achieves greater accuracy for the same aim by combining them in the second method to perform a local search and reassign all image pixels to its cluster properly. With respect to the pigment network detection, the aforementioned pre-processing method is applied, in order to remove most of the hair while keeping the image information and increase the visibility of the pigment network structures. Therefore, a Gabor filter with connected component analysis are used to detect the pigment network lines, before several features are extracted and fed to the Artificial Neural Network as a classifier algorithm. In the lesion classification approach, the K-means is applied to the segmented lesion to separate it into homogeneous clusters, where important features are extracted; then, an Artificial Neural Network with Radial Basis Functions is trained by representative features to classify the given lesion as melanoma or not. The strong experimental results of the lesion border segmentation methods including Fuzzy C-means with Markov Random Field and the combination between the Particle Swarm Optimization and Markov Random Field, achieved an average accuracy of 94.00% , 94.74% respectively. Whereas, the lesion classification stage by using extracted features form pigment network structures and segmented lesions achieved an average accuracy of 90.1% , 95.97% respectively. The results for the entire experiment were obtained using a public database PH2 comprising 200 images. The results were then compared with existing methods in the literature, which have demonstrated that our proposed approach is accurate, robust, and efficient in the segmentation of the lesion boundary, in addition to its classification.

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2

Rolland, Jannick Paule Yvette. "Factors influencing lesion detection in medical imaging." Diss., The University of Arizona, 1990. http://hdl.handle.net/10150/185096.

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An important goal in medical imaging is the assessment of image quality in a way that relates to clinical efficacy. An objective approach is to evaluate the performance of diagnosis for specific tasks, using ROC analysis. We shall concentrate here on classification tasks. While many factors may confine the performance achieved for these tasks, we shall investigate two main limiting factors: image blurring and object variability. Psychophysical studies followed by ROC analysis are widely used for system assessment, but it is of great practical interest to be able to predict the outcome of psychophysical studies, especially for system design and optimization. The ideal observer is often chosen as a standard of comparison for the human observer since, at least for simple tasks, its performance can be readily calculated using statistical decision theory. We already know, however, of cases reported in the literature where the human observer performs far below ideal, and one of the purposes of this dissertation is to determine whether there are other practical circumstances where human and ideal performances diverge. Moreover, when the complexity of the task increases, the ideal observer becomes quickly intractable, and other observers such as the Hotelling and the nonprewhitening (npw) ideal observers may be considered instead. A practical problem where our intuition tells us that the ideal observer may fail to predict human performance occurs with imaging devices that are characterized by a PSF having long spatial tails. The investigation of the impact of long-tailed PSFs on detection is of great interest since they are commonly encountered in medical imaging and even more generally in image science. We shall show that the ideal observer is a poor predictor of human performance for a simple two-hypothesis detection task and that linear filtering of the images does indeed help the human observer. Another practical problem of considerable interest is the effect of background nonuniformity on detectability since, it is one more step towards assessing image quality for real clinical images. When the background is known exactly (BKE), the Hotelling and the npw ideal observers predict that detection is optimal for an infinite aperture; a spatially varying background (SVB) results in an optimum aperture size. Moreover, given a fixed aperture size and a BKE, an increase in exposure time is highly beneficial for both observers. For SVB, on the other hand, the Hotelling observer benefits from an increases in exposure time, while the npw ideal observer quickly saturates. In terms of human performance, results show a good agreement with the Hotelling-observer predictions, while the performance disagrees strongly with the npw ideal observer.

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Nagane, Radhika. "Detection of flash in dermoscopy skin lesion images." Diss., Rolla, Mo. : University of Missouri-Rolla, 2007. http://scholarsmine.umr.edu/thesis/pdf/Nagane_09007dcc803ec3f9.pdf.

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Thesis (M.S.)--University of Missouri--Rolla, 2007.
Vita. The entire thesis text is included in file. Title from title screen of thesis/dissertation PDF file (viewed December 7, 2007) Includes bibliographical references (p. 89-90).

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4

Pons, Rodríguez Gerard. "Computer-aided lesion detection and segmentation on breast ultrasound." Doctoral thesis, Universitat de Girona, 2014. http://hdl.handle.net/10803/129453.

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This thesis deals with the detection, segmentation and classification of lesions on sonography. The contribution of the thesis is the development of a new Computer-Aided Diagnosis (CAD) framework capable of detecting, segmenting, and classifying breast abnormalities on sonography automatically. Firstly, an adaption of a generic object detection method, Deformable Part Models (DPM), to detect lesions in sonography is proposed. The method uses a machine learning technique to learn a model based on Histogram of Oriented Gradients (HOG). This method is also used to detect cancer lesions directly, simplifying the traditional cancer detection pipeline. Secondly, different initialization proposals by means of reducing the human interaction in a lesion segmentation algorithm based on Markov Random Field (MRF)-Maximum A Posteriori (MAP) framework is presented. Furthermore, an analysis of the influence of lesion type in the segmentation results is performed. Finally, the inclusion of elastography information in this segmentation framework is proposed, by means of modifying the algorithm to incorporate a bivariant formulation. The proposed methods in the different stages of the CAD framework are assessed using different datasets, and comparing the results with the most relevant methods in the state-of-the-art
Aquesta tesi es centra en la detecció, segmentació i classificació de lesions en imatges d'ecografia. La contribució d'aquesta tesi és el desenvolupament d'una nova eina de Diagnòstic Assistit per Ordinador (DAO) capaç de detectar, segmentar i classificar automàticament lesions en imatges d'ecografia de mama. Inicialment, s'ha proposat l'adaptació del mètode genèric de detecció d'objectes Deformable Part Models (DPM) per detectar lesions en imatges d'ecografia. Aquest mètode utilitza tècniques d'aprenentatge automàtic per generar un model basat en l'Histograma de Gradients Orientats. Aquest mètode també és utilitzat per detectar lesions malignes directament, simplificant així l'estratègia tradicional. A continuació, s'han realitzat diferents propostes d'inicialització en un mètode de segmentació basat en Markov Random Field (MRF)-Maximum A Posteriori (MAP) per tal de reduir la interacció amb l'usuari. Per avaluar aquesta proposta, s'ha realitzat un estudi sobre la influència del tipus de lesió en els resultats aconseguits. Finalment, s'ha proposat la inclusió d'elastografia en aquesta estratègia de segmentació. Els mètodes proposats per a cada etapa de l'eina DAO han estat avaluats fent servir bases de dades diferents, comparant els resultats obtinguts amb els resultats dels mètodes més importants de l'estat de l'art

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Gonzalez, Ana Guadalupe Salazar. "Structure analysis and lesion detection from retinal fundus images." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/6456.

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Ocular pathology is one of the main health problems worldwide. The number of people with retinopathy symptoms has increased considerably in recent years. Early adequate treatment has demonstrated to be effective to avoid the loss of the vision. The analysis of fundus images is a non intrusive option for periodical retinal screening. Different models designed for the analysis of retinal images are based on supervised methods, which require of hand labelled images and processing time as part of the training stage. On the other hand most of the methods have been designed under the basis of specific characteristics of the retinal images (e.g. field of view, resolution). This compromises its performance to a reduce group of retinal image with similar features. For these reasons an unsupervised model for the analysis of retinal image is required, a model that can work without human supervision or interaction. And that is able to perform on retinal images with different characteristics. In this research, we have worked on the development of this type of model. The system locates the eye structures (e.g. optic disc and blood vessels) as first step. Later, these structures are masked out from the retinal image in order to create a clear field to perform the lesion detection. We have selected the Graph Cut technique as a base to design the retinal structures segmentation methods. This selection allows incorporating prior knowledge to constraint the searching for the optimal segmentation. Different link weight assignments were formulated in order to attend the specific needs of the retinal structures (e.g. shape). This research project has put to work together the fields of image processing and ophthalmology to create a novel system that contribute significantly to the state of the art in medical image analysis. This new knowledge provides a new alternative to address the analysis of medical images and opens a new panorama for researchers exploring this research area.

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Gomez, Bulla Juliana. "Detection, diagnosis and management of the early carious lesion." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/detection-diagnosis-and-management-of-the-early-carious-lesion(f7ae030d-fe41-4e3d-802a-a3cd8c0e978d).html.

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The current evidenced-based caries understanding, based on biological concepts, involves new approaches in caries detection, assessment, and management that should include non-cavitated lesions. The purpose of the studies presented in this thesis was to investigate the current available evidence on methods to detect non-cavitated lesions (NCCls), the current evidence related to the efficacy of non-surgical caries preventive methods to arrest or reverse the progression of NCCls, the current evidence for the prediction of caries using four caries risk assessment systems/guidelines and a review of the literature related to alternative caries clinical trial methods for oral care products. The purpose of the in vitro studies was to study the performance of different caries detection methods (ICDAS, ICDAS photographs, FOTI, QLF, OCT, Soprolife) in detecting early caries lesions and in particular and to assess the QLF ability to detect changes after remineralisation/demineralisation cycles. The last study was a cross-sectional study aiming to investigate the caries management decisions for early caries lesions among dentists. The results of the systematic reviews (Paper I-IV) suggest a large variation of Sensitivity, Specificity and lack of consistence on the definition of disease among the detection methods assessed. The evidence on Caries Risk Assessment Systems is limited and the current systems seem not to predict future disease. In terms of Caries Management, according to the evidence fluorides continue to be the most effectiveness anti-caries agent. The evidence on abbreviated clinical trials showed excellent discrimination between anti-caries products in short clinical trials with fewer subjects using more sensitive caries detection methods. Paper V, showed that all the caries detection methods assessed in this study, except for OCT (0.65), were strongly correlated with Histology. In papers VI and VII, QLF showed the ability to detect differences between two NaF toothpastes (550 ppm F, 1100 ppm F) and a fluoride placebo treatment in two pH cycling models. Finally, the results of the questionnaire on Caries Related Treatment Decisions (Paper VIII) revealed that 60% of the dentists are practising prevention in occlusal early lesions. However, a large number of dentists are still oriented towards a restorative approach and do not base their treatment decisions on individual caries risk. The main conclusions from this thesis are that: 1) A comprehensive management system should include initial caries lesions; 2) Visual examinations is still the standard method of detection, other methods may be included for monitoring purposes; 3) QLF was able to detect remineralisation of artificial carious lesions and inhibition of demineralisation in sound enamel after two remineralisation/demineralisation pH cycling models; 4) The results of the cross-over study indicate that Colombian dentists have not yet fully adopted conservative treatment for early caries lesions.

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Agarwal, Richa. "Computer aided detection for breast lesion in ultrasound and mammography." Doctoral thesis, Universitat de Girona, 2019. http://hdl.handle.net/10803/670295.

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In the field of breast cancer imaging, traditional Computer Aided Detection (CAD) systems were designed using limited computing resources and used scanned films (poor image quality), resulting in less robust application process. Currently, with the advancements in technologies, it is possible to perform 3D imaging and also acquire high quality Full-Field Digital Mammogram (FFDM). Automated Breast Ultrasound (ABUS) has been proposed to produce a full 3D scan of the breast automatically with reduced operator dependency. When using ABUS, lesion segmentation and tracking changes over time are challenging tasks, as the 3D nature of the images make the analysis difficult and tedious for radiologists. One of the goals of this thesis is to develop a framework for breast lesion segmentation in ABUS volumes. The 3D lesion volume in combination with texture and contour analysis, could provide valuable information to assist radiologists in the diagnosis. Although ABUS volumes are of great interest, x-ray mammography is still the gold standard imaging modality used for breast cancer screening due to its fast acquisition and cost-effectiveness. Moreover, with the advent of deep learning methods based on Convolutional Neural Network (CNN), the modern CAD Systems are able to learn automatically which imaging features are more relevant to perform a diagnosis, boosting the usefulness of these systems. One of the limitations of CNNs is that they require large training datasets, which are very limited in the field of medical imaging. In this thesis, the issue of limited amount of dataset is addressed using two strategies: (i) by using image patches as inputs rather than full sized image, and (ii) use the concept of transfer learning, in which the knowledge obtained by training for one task is used for another related task (also known as domain adaptation). In this regard, firstly the CNN trained on a very large dataset of natural images is adapted to classify between mass and non-mass image patches in the Screen-Film Mammogram (SFM), and secondly the newly trained CNN model is adapted to detect masses in FFDM. The prospects of using transfer learning between natural images and FFDM is also investigated. Two public datasets CBIS-DDSM and INbreast have been used for the purpose. In the final phase of research, a fully automatic mass detection framework is proposed which uses the whole mammogram as the input (instead of image patches) and provides the localisation of the lesion within this mammogram as the output. For this purpose, OPTIMAM Mammography Image Database (OMI-DB) is used. The results obtained as part of this thesis showed higher performances compared to state-of-the-art methods, indicating that the proposed methods and frameworks have the potential to be implemented within advanced CAD systems, which can be used by radiologists in the breast cancer screening
En el camp de les imatges de càncer de mama, els sistemes tradicionals de detecció assistida per ordinador (de l’anglès CAD) es van dissenyar utilitzant recursos informàtics limitats i pel·lícules de mamografia escanejades (del angles SFM) de qualitat d’imatge deficient, fet que va resultar en aplicacions poc robustes. Actualment, amb els avanços de les tecnologies, és possible realitzar imatges mèdiques en 3D i adquirir mamografies digitals (de l’anglès FFDM) d’alta qualitat. L’ultrasò automàtic de la mama (de l’anglès ABUS) ha estat proposat per adquirir imatges 3D de la mama amb escassa dependència del operador. Quan s’utilitza ABUS, la segmentació i seguiment de les lesions en el temps s ́on tasques complicades ja que la naturalesa 3D de les imatges fa que l’anàlisi sigui difícil i feixuc per els radiòlegs. Un dels objectius d’aquesta tesi és desenvolupar un marc per la segmentació semi-automàtica de lesions mamàries en volums ABUS. El volum de lesió 3D, en combinació amb l’anàlisi de la textura i el contorn, podria proporcionar informació valuosa per realitzar el diagnòstic radiològic. Tot i que els volums de ABUS són de gran interès, la mamografia de raigs X continua essent la modalitat d’imatge estàndard utilitzada per la detecció precoç del càncer de mama, degut principalment a la seva ràpida adquisició i rendibilitat. A més, amb l’arribada dels mètodes d’aprenentatge profund basats en xarxes neuronals convolucionals (de l’anglès CNN), els sistemes CAD moderns poden aprendre automàticament quines característiques de la imatge són més rellevants per realitzar un diagnòstic, fet que augmenta la utilitat d’aquests sistemes. Una de les limitacions de les CNN és que requereixen de grans conjunts de dades per entrenar, els quals són molt limitats en el camp de la imatge mèdica. En aquesta tesi, el tema de la poca disponibilitat d’imatges mediques s’aborda mitjançant dues estratègies: (i) utilitzant regions de la imatge com a entrada en comptes de les imatges de mida original, i (ii) mitjançant tècniques d’aprenentatge per transferència, en el que el coneixement après per a una determinada tasca es transfereix a una altra tasca relacionada (també conegut com a adaptació de domini). En primer lloc, la CNN entrenada en un conjunt de dades molt gran d’imatges naturals és adaptada per classificar regions de la imatge en tumor i no tumor de SFM i, en segon lloc, la CNN entrenada és adaptada per detectar tumors en FFDM. També s’ha investigat l’aprenentatge per transferència entre imatges naturals i FFDM. S’han utilitzat dos conjunts de dades públiques (CBIS-DDSM i INbreast) per aquest propòsit. En la fase final de la investigació, es proposa un marc de detecció automàtica de tumors utilitzant la mamografia original com entrada (en lloc de regions de la imatge) i que proporciona la localització de la lesió dins d’aquesta mamografia com a sortida. Per aquest propòsit s’utilitza una altra base de dades (OMI-DB). Els resultats obtinguts com a part d’aquesta tesi mostren millors rendiments en comparació amb l’estat de l’art, el que indica que els mètodes i marcs proposats tenen el potencial de ser implementats dins de sistemes CAD avançats, que poden ser utilitzats per radiòlegs en el cribratge del càncer de mama

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Yap, Moi Hoon. "Enhanced algorithms for lesion detection and recognition in ultrasound breast images." Thesis, Loughborough University, 2008. https://dspace.lboro.ac.uk/2134/35018.

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Mammography is the gold standard for breast cancer detection. However, it has very high false positive rates and is based on ionizing radiation. This has led to interest in using multi-modal approaches. One modality is diagnostic ultrasound, which is based on non-ionizing radiation and picks up many of the cancers that are generally missed by mammography. However, the presence of speckle noise in ultrasound images has a negative effect on image interpretation. Noise reduction, inconsistencies in capture and segmentation of lesions still remain challenging open research problems in ultrasound images. The target of the proposed research is to enhance the state-of-art computer vision algorithms used in ultrasound imaging and to investigate the role of computer processed images in human diagnostic performance.

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Alaverdyan, Zaruhi. "Unsupervised representation learning for anomaly detection on neuroimaging. Application to epilepsy lesion detection on brain MRI." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI005/document.

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Cette étude vise à développer un système d’aide au diagnostic (CAD) pour la détection de lésions épileptogènes, reposant sur l’analyse de données de neuroimagerie, notamment, l’IRM T1 et FLAIR. L’approche adoptée, introduite précédemment par Azami et al., 2016, consiste à placer la tâche de détection dans le cadre de la détection de changement à l'échelle du voxel, basée sur l’apprentissage d’un modèle one-class SVM pour chaque voxel dans le cerveau. L'objectif principal de ce travail est de développer des mécanismes d’apprentissage de représentations, qui capturent les informations les plus discriminantes à partir de l’imagerie multimodale. Les caractéristiques manuelles ne sont pas forcément les plus pertinentes pour la tâche visée. Notre première contribution porte sur l'intégration de différents réseaux profonds non-supervisés, pour extraire des caractéristiques dans le cadre du problème de détection de changement. Nous introduisons une nouvelle configuration des réseaux siamois, mieux adaptée à ce contexte. Le système CAD proposé a été évalué sur l’ensemble d’images IRM T1 des patients atteints d'épilepsie. Afin d'améliorer la performance obtenue, nous avons proposé d'étendre le système pour intégrer des données multimodales qui possèdent des informations complémentaires sur la pathologie. Notre deuxième contribution consiste donc à proposer des stratégies de combinaison des différentes modalités d’imagerie dans un système pour la détection de changement. Ce système multimodal a montré une amélioration importante sur la tâche de détection de lésions épileptogènes sur les IRM T1 et FLAIR. Notre dernière contribution se focalise sur l'intégration des données TEP dans le système proposé. Etant donné le nombre limité des images TEP, nous envisageons de synthétiser les données manquantes à partir des images IRM disponibles. Nous démontrons que le système entraîné sur les données réelles et synthétiques présente une amélioration importante par rapport au système entraîné sur les images réelles uniquement
This work represents one attempt to develop a computer aided diagnosis system for epilepsy lesion detection based on neuroimaging data, in particular T1-weighted and FLAIR MR sequences. Given the complexity of the task and the lack of a representative voxel-level labeled data set, the adopted approach, first introduced in Azami et al., 2016, consists in casting the lesion detection task as a per-voxel outlier detection problem. The system is based on training a one-class SVM model for each voxel in the brain on a set of healthy controls, so as to model the normality of the voxel. The main focus of this work is to design representation learning mechanisms, capturing the most discriminant information from multimodality imaging. Manual features, designed to mimic the characteristics of certain epilepsy lesions, such as focal cortical dysplasia (FCD), on neuroimaging data, are tailored to individual pathologies and cannot discriminate a large range of epilepsy lesions. Such features reflect the known characteristics of lesion appearance; however, they might not be the most optimal ones for the task at hand. Our first contribution consists in proposing various unsupervised neural architectures as potential feature extracting mechanisms and, eventually, introducing a novel configuration of siamese networks, to be plugged into the outlier detection context. The proposed system, evaluated on a set of T1-weighted MRIs of epilepsy patients, showed a promising performance but a room for improvement as well. To this end, we considered extending the CAD system so as to accommodate multimodality data which offers complementary information on the problem at hand. Our second contribution, therefore, consists in proposing strategies to combine representations of different imaging modalities into a single framework for anomaly detection. The extended system showed a significant improvement on the task of epilepsy lesion detection on T1-weighted and FLAIR MR images. Our last contribution focuses on the integration of PET data into the system. Given the small number of available PET images, we make an attempt to synthesize PET data from the corresponding MRI acquisitions. Eventually we show an improved performance of the system when trained on the mixture of synthesized and real images

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Slimani, Amel. "Photonic approach for the study of dental hard tissues and carious lesion detection." Thesis, Montpellier, 2017. http://www.theses.fr/2017MONTT125.

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Les propriétés photoniques des tissus durs dentaires nous ont permis d’étudier l’email et la dentine a un niveau moléculaire (in vitro) en utilisant des techniques de microscopie optique non linéaires. La microscopie confocale Raman est technique d’imagine de haute résolution permettant d’analyse d’échantillon sans préparation spécifique ni marquage. Cette méthode nous a permis de reconstituer une cartographie de la réticulation du collagène et de la cristallinité au niveau de la jonction émail-dentine et cela avec une résolution spatiale non atteinte jusque-là. Cette analyse chimique de la jonction émail-dentine a permis de redéfinir la largeur de cette zone de transition. Cette largeur est nettement supérieure à celles proposées par les études précédentes. Par ailleurs, l’étude portant sur les changements de fluorescence intrinsèque entre les tissues dentaires sains et cariés suggèrent l’implication de la protoporphyrin IX et de la pentosidine dans l’expression de la fluorescence rouge des tissus cariés. La microscopie multiphotonique quant à elle nous a permis de détecter la lésion carieuse et de suivre son développement en utilisant la génération de seconde harmonique (SHG) et la fluorescence par excitation à deux photons (2PEF). Nos études ont démontré la validité du ratio SHG/2PEF comme paramètre fiable pour la détection de la lésion carieuse. Les études proposées par cette thèse montrent le potentiel des propriétés photoniques de l’émail et de la dentine en utilisant les microscopies Raman et multiphotoniques dans l’étude de ces tissus au niveau moléculaire. Cela offre de nouvelles perspectives en recherche et en applications cliniques
Photonic properties of dental hard tissues allowed us to proceed to in vitro analysis of enamel and dentin on a molecular level. Confocal Raman microscopy has been used to produce a mapping of collagen cross-link and crystallinity of human dentin–enamel junction (DEJ) with a spatial resolution not achieved up to now. The method is a non-invasive, label-free and a high spatial resolution imaging technique. This chemical analysis of DEJ led us to redefine a wider width of this transition zone and advance our understanding of dental histology. A study on the intrinsic fluorescence changes of sound and carious tissues using conventional fluorescence microscopy suggests the involvement of protoporphyrin IX and pentosidine in the fluorescence red-shift observed in carious tissues. Multiphoton microscopy allowed to detect nonlinear optical signal changes during caries process using second harmonic generation (SHG) and two-photon excitation fluorescence (2PEF). Our studies led us to propose the ratio SHG/2PEF as valuable parameter to monitor caries lesion. Collectively, advances described in this thesis show the potential of photonic properties of enamel and dentin using Raman and multiphoton microcopies for molecular investigations on sound as much as on carious tissues. It opens new perspective in dental research and clinical applications

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Dong, Xu. "Segmenting Skin Lesion Attributes in Dermoscopic Images Using Deep Learing Algorithm for Melanoma Detection." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/86883.

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Melanoma is the most deadly form of skin cancer worldwide, which causes the 75% of deaths related to skin cancer. National Cancer Institute estimated that 91,270 new case and 9,320 deaths are expected in 2018 caused by melanoma. Early detection of melanoma is the key for the treatment. The image technique to diagnose skin cancer is dermoscopy, which leads to improved diagnose accuracy compared to traditional ABCD criteria. But reading and examining dermoscopic images is a time-consuming and complex process. Therefore, computerized analysis methods of dermoscopic images have been developed to assist the visual interpretation of dermoscopic images. The automatic segmentation of skin lesion attributes is a key step in computerized analysis of dermoscopic images. The International Skin Imaging Collaboration (ISIC) hosted the 2018 Challenges to help the diagnosis of melanoma based on dermoscopic images. In this thesis, I develop a deep learning based approach to automatically segment the attributes from dermoscopic skin lesion images. The approach described in the thesis achieved the Jaccard index of 0.477 on the official test dataset, which ranked 5th place in the challenge.
Master of Science
Melanoma is the most deadly form of skin cancer worldwide, which causes the 75% of deaths related to skin cancer. Early detection of melanoma is the key for the treatment. The image technique to diagnose skin cancer is called dermoscopy. It has become increasingly conveniently to use dermoscopic device to image the skin in recent years. Dermoscopic lens are available in the market for individual customer. When coupling the dermoscopic lens with smartphones, people are be able to take dermoscopic images of their skin even at home. However, reading and examining dermoscopic images is a time-consuming and complex process. It requires specialists to examine the image, extract the features, and compare with criteria to make clinical diagnosis. The time-consuming image examination process becomes the bottleneck of fast diagnosis of melanoma. Therefore, computerized analysis methods of dermoscopic images have been developed to promote the melanoma diagnosis and to increase the survival rate and save lives eventually. The automatic segmentation of skin lesion attributes is a key step in computerized analysis of dermoscopic images. In this thesis, I developed a deep learning based approach to automatically segment the attributes from dermoscopic skin lesion images. The segmentation result from this approach won 5th place in a public competition. It has the potential to be utilized in clinic application in the future.

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Kretzler, Madison Elizabeth. "AUTOMATED CURVED HAIR DETECTION AND REMOVAL IN SKIN IMAGES TO SUPPORT AUTOMATED MELANOMA DETECTION." Case Western Reserve University School of Graduate Studies / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=case1365125074.

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13

Zachariah, Cherian Renil. "Statistical Model for Predicting Multiple Sclerosis Cortical Lesion Detection Rates with Ultra High Field Imaging." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1293726279.

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14

Montanari, Giovanni. "Deep Transfer Learning for Automated Detection of Spinal Lesions from CT scans." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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In this thesis we implement an automated Computer-Aided Detection (CADe) system for spine lesions using Computed Tomographies (CTs) and Convolutional Neural Networks (CNNs). To this end, we conceptualize an algorithmic approach for the whole process of extraction and processing of the vertebrae from CT scans, which also manages the detection step for the whole vertebral body. For training and testing purposes, we generated a dataset composed of several CTs in collaboration with the Rizzoli Orthopaedic Insitute of Bologna, Italy. The vertebrae, either healthy or containing lesions (e.g. metastases, primary tumors, lytic and sclerotic lesions) were extracted from CT scans with a toolbox developed ad hoc to automatize the process. The resulting dataset is composed of slices from the previously extracted volumes containing the vertebrae. Slices were processed with contrast enhancement and data augmentation techniques, and subsequently used to train the Neural Network. For the purpose of detection, we perform an in-depth comparative study by implementing 4 pre-trained networks and exploiting Transfer Learning techniques. To prove the great advantages of Transfer Learning, we show how the pre-trained networks outperform a network trained from scratch, reaching 95.97% accuracy and F1 score of 94.22%. Finally, we equip the CADe system with an intuitive Graphical User Interface (GUI) to allow physicians to use the automated detection software as a support tool for diagnoses on new patients.

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15

Dhinagar, Nikhil J. "Morphological Change Monitoring of Skin Lesions for Early Melanoma Detection." Ohio University / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1533911373953079.

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16

Salem, Mostafa. "Deep learning methods for automated detection of new multiple sclerosis lesions in longitudinal magnetic resonance images." Doctoral thesis, Universitat de Girona, 2020. http://hdl.handle.net/10803/668990.

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This thesis is focused on developing novel and fully automated methods for the detection of new multiple sclerosis (MS) lesions in longitudinal brain magnetic resonance imaging (MRI). First, we proposed a fully automated logistic regression-based framework for the detection and segmentation of new T2-w lesions. The framework was based on intensity subtraction and deformation field (DF). Second, we proposed a fully convolutional neural network (FCNN) approach to detect new T2-w lesions in longitudinal brain MR images. The model was trained end-to-end and simultaneously learned both the DFs and the new T2-w lesions. Finally, we proposed a deep learning-based approach for MS lesion synthesis to improve the lesion detection and segmentation performance in both cross-sectional and longitudinal analysis
Esta tesis se centra en el desarrollo de métodos novedosos y totalmente automatizados para la detección de nuevas lesiones de esclerosis múltiple en la resonancia magnética longitudinal del cerebro. Primero, propusimos un marco totalmente automatizado basado en la regresión logística para la detección y segmentación de nuevas lesiones T2-w. El marco se basaba en la sustracción de intensidad y el campo de deformación (DF). En segundo lugar, propusimos un enfoque de red neuronal totalmente convolucional para detectar nuevas lesiones T2-w en imágenes de resonancia magnética del cerebro longitudinal. El modelo se entrenó de extremo a extremo y aprendió simultáneamente tanto los DF como las nuevas lesiones T2-w. Por último, propusimos un enfoque basado en el aprendizaje profundo para la síntesis de las lesiones de la EM, a fin de mejorar el rendimiento de la detección y la segmentación de las lesiones tanto en el análisis transversal como en el longitudinal

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17

Jones, Kristi L. "Saccharomyces Cerevisiae as a Model Organism to Delineate Initial Lesion Detection Events in Chromatin Repair: A Focus On Ddb2-Mediated GG-NER." Scholarly Repository, 2011. http://scholarlyrepository.miami.edu/oa_dissertations/584.

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DNA damage repair is an essential and complex cellular process. Although the basic mechanisms of nucleotide excision repair (NER) have been studied for decades, some mechanistic details remain elusive. The lesion detection step remains one of the most elusive in the process of NER in the contest of chromatin. The work described herein addresses the initial events in the lesion detection step of chromatin repair, also referred to as global genome repair (GG-NER). Both the role of post-translational modifications of lesion identification proteins, and the initial sequence of events in recruitment of repair and remodeling factors are investigated. First, the controversial role of ubiquitination of DDB2 (a human lesion detection protein) is investigated. Due to documented DDB2 function in alternative physiological processes, its direct role in GG-NER is hard to study in human cells. To overcome this obstacle, we established the budding yeast, Saccharomyces cerevisiae as an alternative, simplified model organism to study DDB2-mediated GG-NER. Using this system, we show that inconsistent with the widely accepted model, rapid degradation of DDB2 post-UV irradiation is not an absolute requirement for progression of GG-NER. However, interestingly, our data suggest a role for ubiquitination in the release of DDB2 from chromatin. In both UV and mock treated samples, ubiquitin deficient cells had significantly higher amounts of DDB2 remaining bound to the chromatin compared to the isogenic parent cells. The discussion focuses on the possible physiological relevance of these observations. Additionally, the recruitment of the SWI/SNF chromatin remodeling complex to the silent HML (Hidden MAT Left) locus was also investigated. SWI/SNF is known to require recruitment for its role in transcription; therefore we investigate this requirement in GG-NER. Based on previously published data that indicate an UV-stimulated association of SWI/SNF and Rad4 (a lesion detection protein), we hypothesized that Rad4 is involved in recruitment of SWI/SNF to damaged DNA. Interestingly, our data suggest that Rad4 is not an absolute requirement for recruitment of Snf6 to the HML locus following UV irradiation. However, Rad16 appears to be. These data present an interesting insight into the lesion detection step in GG-NER and this will be discussed.

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18

Sharma, Sanjay. "The accuracy of visible retinal emboli for the detection of a hemodynamically significant carotid artery lesion in the setting of acute retinal occlusion." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp04/mq20696.pdf.

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19

Ohno, Tsuyoshi. "Usefulness of breath-hold inversion recovery-prepared T1-weighted two-dimensional gradient echo sequence for detection of hepatocellular carcinoma in Gd-EOB-DTPA-enhanced MR imaging." Kyoto University, 2017. http://hdl.handle.net/2433/218009.

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20

Fusco, Roberta <1985&gt. "Lesion detection and classification in breast cancer: evaluation of approaches based on morphological features, tracer kinetic modelling and semi-quantitative parameters in MR functional imaging (DCE-MRI)." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2013. http://amsdottorato.unibo.it/5302/.

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The diagnosis, grading and classification of tumours has benefited considerably from the development of DCE-MRI which is now essential to the adequate clinical management of many tumour types due to its capability in detecting active angiogenesis. Several strategies have been proposed for DCE-MRI evaluation. Visual inspection of contrast agent concentration curves vs time is a very simple yet operator dependent procedure, therefore more objective approaches have been developed in order to facilitate comparison between studies. In so called model free approaches, descriptive or heuristic information extracted from time series raw data have been used for tissue classification. The main issue concerning these schemes is that they have not a direct interpretation in terms of physiological properties of the tissues. On the other hand, model based investigations typically involve compartmental tracer kinetic modelling and pixel-by-pixel estimation of kinetic parameters via non-linear regression applied on region of interests opportunely selected by the physician. This approach has the advantage to provide parameters directly related to the pathophysiological properties of the tissue such as vessel permeability, local regional blood flow, extraction fraction, concentration gradient between plasma and extravascular-extracellular space. Anyway, nonlinear modelling is computational demanding and the accuracy of the estimates can be affected by the signal-to-noise ratio and by the initial solutions. The principal aim of this thesis is investigate the use of semi-quantitative and quantitative parameters for segmentation and classification of breast lesion. The objectives can be subdivided as follow: describe the principal techniques to evaluate time intensity curve in DCE-MRI with focus on kinetic model proposed in literature; to evaluate the influence in parametrization choice for a classic bi-compartmental kinetic models; to evaluate the performance of a method for simultaneous tracer kinetic modelling and pixel classification; to evaluate performance of machine learning techniques training for segmentation and classification of breast lesion.

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21

Basic, Varas Franna. "Validación de la herramienta R2 lesion metrics, del software computer aided detection imagechecker 9.0, mediante la correlación de las microcalcificaciones, el resultado histopatológico y el score del cad." Tesis, Universidad de Chile, 2017. http://repositorio.uchile.cl/handle/2250/147302.

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Grado de magíster en informática médica
El cáncer de mama (CM), es una de las principales causas de muerte en mujeres a nivel mundial. El número de falsos positivos (FP) y falsos negativos (FN) que resultan del diagnóstico mamográfico constituyen los errores diagnósticos más frecuentes. La herramienta utilizada en mamografía Computer Aided Detection (CAD), puede resultar ser el avance más significativo en la detección del CM en los últimos 25 años. El propósito de esta tesis es identificar si el parámetro numérico (Score) que entrega el CAD tiene directa relación con el resultado histopatológico de las microcalcificaciones. Todo esto con el objetivo de contribuir a la toma de decisiones por parte del médico al momento de la clasificación de las lesiones conducentes a biopsias y, con ello, mejorar los resultados en el informe mamográfico y el número de las solicitudes de estudio histopatológico. El diseño de investigación empleado fue retrospectivo de cohorte transversal. El período para la toma de la muestra abarcó desde enero hasta diciembre de 2016 y se consideraron las variables: Score, microcalcificaciones, resultado histopatológico y BIRADS. Los resultados obtenidos dieron cuenta de la existencia de una correlación entre el Score, la clasificación BIRADS 4 y el resultado de biopsia, pero no en forma absoluta, ya que se obtuvo un 21% de FP. Sin embargo, la correlación evaluada presentó potencial, demostrando una alta especificidad para la detección de lesiones mamarias.
Breast cancer is one of the leading causes of death in women worldwide. The number of false positives (FP) and false negatives (FN) in mammographic diagnosis represent the most frequent diagnostic errors. Health informatics tool named Computer Aided Detection (CAD), may prove be the most significant progress for breast cancer detection in the last 25 years. The purpose of this thesis is to identify if the numerical parameter (Score) that is delivered by CAD has a direct relation with the microcalcifications histopathological results. All this with the aim of contributing to the decision making by the physician at the time of the classification of the lesions leading to biopsies and, with that, to improve the results in the mammographic report and the number of the histopathological study requests. This was a retrospective cross-sectional cohort investigation. The collection of the sample ranged from January to September 2016, and the variables considered were: Score, microcalcificacions, histopathological findings and BIRADS. The results obtained showed the existence of a correlation between the Score, the BIRADS 4 classification and the biopsy result, but not in absolute form, since 21% of FP was obtained. However, the correlation evaluated presented potential, demonstrating a high specificity for the detection of breast lesions.

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Barros, Netto Stelmo Magalhães. "Métodos computacionais para identificação, quantificação e análise de mudanças no tecido da lesão pulmonar através de imagens de tomografia computadorizada." Universidade Federal do Maranhão, 2016. http://tedebc.ufma.br:8080/jspui/handle/tede/1700.

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Submitted by Rosivalda Pereira (mrs.pereira@ufma.br) on 2017-06-26T19:30:57Z No. of bitstreams: 1 Stelmo.pdf: 9433038 bytes, checksum: 2b73bb4f0f32aec1145044fb676465e6 (MD5)
Made available in DSpace on 2017-06-26T19:30:57Z (GMT). No. of bitstreams: 1 Stelmo.pdf: 9433038 bytes, checksum: 2b73bb4f0f32aec1145044fb676465e6 (MD5) Previous issue date: 2016-10-17
Lung cancer is one of the most common types of cancer around the world. Temporal evaluation has become a very useful tool when to whoever needs to analyze a lung lesion. The analysis occurs when a malignant lesion is under treatment or when there are indeterminate lesions, but they are probably benign. The objective from this work is to develop computational methods to detect, quantify and analyze local and global density changes of pulmonary lesions over time. Thus, it were developed four groups of methods to perform this task. The rst identi es local density changes and it has been denominated voxel-based. The second one is composed of the Jensen divergence and the hypothesis test with global and local approaches. Similarly, the third group has only one method, the principal component analysis. The last group has one method, it has been denominated modi ed quality threshold, and identi es the local density changes. In order to reach the objectives, it was proposed a methodology composed of ve steps: The rst step consists in image acquisition of the lesion at various instants. Two image databases were acquired and two models of lesions were created to evaluate the methods. The rst database has 24 lesions under treatment (public database) and the second has 13 benign nodules (private database) in monitoring. The second step refers to rigid registration of the lesion images. The next step is to apply the proposed four groups of methods. As a result, the second group of methods detected more density changes than the fourth group, which in turn, this latter detected more regions than the rst group and this more than the third group, for the public database. For the private database, the fourth group of density change methods detected more regions than the rst group. The third group detected few regions of changes when compared to the rst group and the second group had the lowest number of detected regions. In addition to the density changes found, the proposed classi cation model with texture features had accuracy above 98% in the diagnosis prediction. The results state that there are changes in both databases. However, the detected changes for each group of methods have di erent intensity and location to the databases. This conclusion is based from high accuracy that was obtained from the prediction of the lesion diagnosis from both databases.
O câncer de pulmão é um dos tipos de câncer de maior incidência no mundo. A avaliação temporal aparece como ferramenta bastante útil quando se deseja analisar uma lesão. A análise pode ocorrer quando uma lesão maligna está em tratamento ou quando surgem lesões indeterminadas, mas essas são provavelmente benignas. O objetivo deste trabalho é desenvolver métodos computacionais para detectar, quantifi car e analisar mudanças de densidade locais e globais das lesões pulmonares ao longo do tempo. Desta forma, foram desenvolvidos quatro conjuntos de métodos para realização da tarefa de detectar mudanças de densidade em lesões pulmonares. O primeiro conjunto identifi ca mudanças de densidade locais e foi denominado de métodos baseados em voxel. O segundo conjunto é composto da divergência de Jensen e do teste de hipótese com abordagens locais e globais. Com o mesmo propósito de detectar mudanças de densidade locais em lesões pulmonares, o terceiro conjunto possui um único método, a análise de componentes principais. O último conjunto também possui um único método, denominado de quality threshold modi ficado e identifi ca as mudanças locais de densidade. Para cumprir o objetivo deste trabalho, propõe-se uma metodologia composta de cinco etapas. A primeira etapa consiste na aquisição das imagens da lesão em diversos instantes. Duas bases de lesões foram utilizadas e dois modelos de lesões foram propostos para avaliação dos métodos. A primeira base possui 24 lesões em tratamento (base pública) e a segunda possui 13 nódulos benignos (base privada) em acompanhamento. A segunda etapa corresponde ao registro rígido das imagens da lesão. A próxima etapa é a aplicação dos quatro conjuntos de métodos propostos. Como resultado, o segundo conjunto de métodos detectou mais mudanças de densidade que o quarto conjunto, que por sua vez, este ultimo detectou mais regões que o primeiro conjunto e este mais que o terceiro conjunto, para a base pública de lesões. Em relação a base privada, o quarto conjunto de métodos detectou mais regiões de mudança de densidade que o primeiro conjunto. O terceiro conjunto detectou menos regiões de mudança quando comparado ao primeiro conjunto e o segundo conjunto teve o menor n úmero de regiões detectadas. Em adição às mudanças de densidade encontradas, o modelo de classi ficação proposto com medidas clássicas de textura para predição do diagnóstico da lesão teve acurácia acima de 98%. Os resultados encontrados indicam que existem mudanças de densidade em ambas as bases de lesões pulmonares. Entretanto, as mudanças detectadas por cada um dos métodos propostos possuem características de intensidade e localização diferentes em ambas as bases. Essa conclusão é motivada pela alta acurácia obtida em seu diagnóstico para as bases utilizadas.

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Wirth, Anna Maria [Verfasser], and Mark W. [Akademischer Betreuer] Greenlee. "Structural magnetic resonance imaging in amyotrophic lateral sclerosis: cortical morphometry, diffusion properties and lesion detection as potential biomarkers for the state and progression of amyotrophic lateral sclerosis / Anna Maria Wirth ; Betreuer: Mark W. Greenlee." Regensburg : Universitätsbibliothek Regensburg, 2019. http://d-nb.info/1188026658/34.

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24

Biggar, Heather Caroline. "Experiences from detection to diagnosis : lessons learned from patients with high-risk oral lesions." Thesis, University of British Columbia, 2009. http://hdl.handle.net/2429/3980.

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Oral cancer is the 6th most common cancer in the world, with a poor prognosis and frequent late-stage diagnosis, which significantly impacts survival and quality of life. The key to better control of this disease is early detection, preferably at a precancerous stage. In order to facilitate this early detection and diagnosis, it is critical to identify the factors potentially impacting on the time lag from initial detection to diagnostic biopsy. The overall goal is to develop effective strategies for early identification of oral cancers in order to achieve better control over this disease. There are 2 components in this thesis: the objectives of part I (personal interview) were 1) to develop an interview-style questionnaire, 2) to collect data from patients with high-risk oral lesions (HRL’s) and 3) to characterize the experiences of these individuals that may have impacted the time interval leading up to diagnosis. The objectives of Part II (focus group discussion) were 1) to gather feedback regarding the questionnaire developed in Part I, 2) to obtain recommendations for future planning and delivery of province-wide questionnaire and 3) as a group, to share information on patients’ experiences to diagnosis and patients’ perspectives on their interactions with health professionals (HP’s) throughout this journey. An interview-style questionnaire was developed to collect both qualitative and quantitative data on patients’ experiences. Forty patients with HRL’s diagnosed within 12 months were recruited and interviewed in the Dysplasia Clinic of the BC Oral Cancer Prevention Program. Two focus groups were conducted and feedback from participants regarding the questionnaire and patients’ experiences was recorded. Among 40 patients interviewed, 21 (53%) initially self-identified their lesions (SIG) and 19 (47%) were identified by health professional screening (PSG; 84% by dental professionals). The SIG showed higher rates of invasive SCC at diagnosis as compared to those in the PSG (76% vs. 32%, P = 0.01) and SIG took twice as long to have the initial biopsy performed as the PSG (23 ± 52 vs. 11 ± 28 months). Notably, the main symptom of patients in SIG was pain or presence of non-healing ulcers (18/21; 86%). In contrast, all lesions in PSG were asymptomatic. The mean time from detection to diagnosis was 17.5 ± 42.3 months (range: 0-240 months). Fourteen patients (35%) experienced a time lag of greater than 6 months from first detection of an oral lesion until the first diagnostic biopsy was performed. Both patient and professional factors impact on the time lag. The main contributing factors for this time lag include both patient factors (a lack of concern, fear, and a lack of oral cancer awareness) and the professional factors (lack of knowledge in differentiating high-risk lesions, delay in initiating the referral or ‘watch and wait’, and delay in scheduling of referral appointments to the specialists). Focus group results supported the format and content of the questionnaire, provided input in designing of future province-wide survey and emphasized that patients require continued post-diagnostic and treatment care. A general lack of awareness of oral cancer in general population and in HP’s in addition to a lack of screening activities have been brought forward as critical factors that result in delay to diagnosis. In conclusion, these results suggest HP’s, especially dental professionals, can play a critical role in early identification of HRL’s at an asymptomatic, pre-invasive stage through regular screening. Strategies in raising awareness of oral cancer in both the general population and among HP’s are essential for early identification of oral cancers in order to achieve better control over this disease.

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Romero, Juan Sebastian Lara. "Impacto de um modelo 3D da formação e progressão de lesões de cárie como objeto de aprendizagem no treinamento/ensino de alunos de graduação de diferentes contextos, na detecção de lesões de cárie utilizando o ICDAS: estudo multicêntrico controlado randomizado." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/23/23132/tde-04102016-151055/.

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O presente estudo teve como objetivo avaliar o impacto de um modelo 3D, sobre a formação e progressão de lesões de cárie como objeto de aprendizagem, no desempenho teórico/prático e grau de satisfação de alunos de graduação em odontologia de diferentes contextos, na detecção de lesões de cárie utilizando o ICDAS. Foi conduzido um estudo multicêntrico controlado randomizado envolvendo uma amostra por conveniência de alunos de graduação em odontologia de cinco instituições (1 nacional e 4 internacionais). Inicialmente, os alunos receberam uma aula teórica tradicional e responderam uma primeira avaliação teórica. Posteriormente, foram aleatoriamente alocados em dois grupos: 1) grupo teste: que recebeu uma aula teórica tradicional expositiva mais acesso ao modelo 3D e 2) grupo controle: que recebeu unicamente a aula teórica tradicional expositiva. Depois, os alunos do grupo controle saíram da sala e um vídeo de 6 minutos (modelo 3D) foi projetado. Após o vídeo os alunos do grupo controle regressaram à sala e ambos os grupos foram submetidos a uma avaliação teórico/prática com o propósito de avaliar o desempenho após a intervenção e grau de satisfação da atividade. Análises de regressão linear e de Poisson multinível foram realizadas para analisar o impacto do objeto de aprendizagem no desempenho teórico-prático do aluno. Análises descritivas foram realizadas para avaliar o grau de satisfação do aluno. Um total de 307 alunos participou do estudo. Alunos que tiveram melhor desempenho na avaliação teórica inicial obtiveram melhores notas na média teórica final (OR=1,11; 95%IC=1,02-1,21). Alunos do grupo teste tiveram um melhor desempenho teórico em comparação com os do grupo controle (p=0,04), principalmente para questões relacionadas à correlação histológica do ICDAS com características clínicas dos diferentes estágios de progressão. Não houve diferença estatisticamente significante na avaliação prática entre grupos e, um alto nível de satisfação da atividade foi observado na amostra. Conclui-se que, a atividade avaliada teve um impacto satisfatório no desenvolvimento de competências teóricas relacionadas à detecção de lesões de cárie utilizando o ICDAS.
This study aimed at evaluating the impact of a 3D model as a learning object in the training/teaching and satisfaction degree of undergraduate dental students from different contexts for the detection of caries lesions using the ICDAS. A multicenter controlled randomized trial was conducted, involving a convenience sample of undergraduate dental students from five institutions (1 national and 4 international). Firstly, students attended a traditional theoretical lecture and answered a first theoretical test. Then, they were randomly allocated into two groups as follows: 1) test group: receiving the theoretical lecture and accessing the 3D model, and 2) control group: receiving the theoretical lecture only. Afterwards, control group students left the room and a 6-minute video was projected (3D model). Once the video had finished, control group students returned to the room and both groups were submitted to a theoretical/practical test to evaluate their performance after intervention as well as their satisfaction degree. Multilevel linear and Poisson regression analyses were done, to analyze the learning object impact in the students´ theoretical/practical performance. Descriptive analyses were conducted to assess the students´ satisfaction degree. Three hundred and seven students participated. Those having a better performance in the initial theoretical test also had better grades in the final theoretical assessment (OR=1,11; 95%IC=1,02-1,21). Test group students had a better theoretical performance in comparison to control group ones (p=0,04), mainly in relation to questions regarding the ICDAS histological correlation with clinical features on each severity caries stage. There were no statistically significant differences regarding practical assessment between groups, and a high level of activity satisfaction was observed. In conclusion, the assessed activity had a satisfactory impact in the developing of theoretical skills in relation to the detection of caries lesions using the ICDAS.

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26

Chmelík, Jiří. "Metody detekce, segmentace a klasifikace obtížně definovatelných kostních nádorových lézí ve 3D CT datech." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2020. http://www.nusl.cz/ntk/nusl-433066.

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The aim of this work was the development of algorithms for detection segmentation and classification of difficult to define bone metastatic cancerous lesions from spinal CT image data. For this purpose, the patient database was created and annotated by medical experts. Successively, three methods were proposed and developed; the first of them is based on the reworking and combination of methods developed during the preceding project phase, the second method is a fast variant based on the fuzzy k-means cluster analysis, the third method uses modern machine learning algorithms, specifically deep learning of convolutional neural networks. Further, an approach that elaborates the results by a subsequent random forest based meta-analysis of detected lesion candidates was proposed. The achieved results were objectively evaluated and compared with results achieved by algorithms published by other authors. The evaluation was done by two objective methodologies, technical voxel-based and clinical object-based ones. The achieved results were subsequently evaluated and discussed.

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27

Barlow, Thomas. "Immunochemical detection of mutagenic lesions in DNA." Thesis, University of Edinburgh, 1995. http://hdl.handle.net/1842/10737.

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Vinyl chloride, a widely used industrial precursor is an established chemical carcinogen. It is metabolically activated to forms which alkylate DNA, producing a range of nucleic acid adducts. Resultant mutagenesis occurs almost exclusively at cytosines which are converted to the exocyclic lesion, 3,N⁴ ethenodeoxycytidine (C). A monoclonal antibody which specifically recognises C has been developed. The specificity and affinity of this antibody has been determined by ELISA. Immunoaffinity gels have been prepared and an immunoenrichment procedure developed to detect and identify C from normal nucleotides at very low abundance. Immunopurified samples of C have been quantified by CZE on addition of a standard spike with detection sensitivity of 1 C in 10⁸normal nucleotides. Kinetic studies using this detection system found production of C to be greater in single stranded DNA than in double stranded DNA when incubated with a metabolite of vinyl chloride. Oligonucleotides incorporating C have been synthesised and used in thermodynamic and structural studies to investigate the base pairing effect of C in double stranded DNA. C was found to have a detrimental effect on duplex stability although the overall duplex structure was found to be undistorted. Oligonucleotides labelled with the dansyl group have been synthesised for use as gene probes. A monoclonal antibody with affinity to dansyl was developed to immunochemically detect this reporter group. Femtomole quantities of labelled probe were detected with this system.

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Korotkov, Konstantin. "Automatic change detection in multiple pigmented skin lesions." Doctoral thesis, Universitat de Girona, 2014. http://hdl.handle.net/10803/260162.

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Malignant melanoma is the rarest and deadliest of skin cancers causing three times more deaths than all other skin-related malignancies combined. Fortunately, in its early stages, it is completely curable, making a total body skin examination (TBSE) a fundamental procedure for many patients. Despite the advances in body scanning techniques, automated assistance tools for TBSEs have not received due attention. This fact is emphasized in our literature review covering the area of computerized analysis of PSL images. Aiming at the automation of TBSEs, we have designed and built a total body scanner to acquire skin surface images using cross-polarized light. Furthermore, we have developed an algorithm for the automated mapping of PSLs and their change estimation between explorations. The initial tests of the scanner showed that it can be successfully applied for automated mapping and temporal monitoring of multiple lesions
El melanoma maligne és el més rar i mortal de tots els càncers de pell, causant tres vegades més morts que el conjunt de totes les altres malalties malignes de la pell. Afortunadament, en les primeres etapes, és completament curable, fent de les exploracions de pell a nivell de cos complert (TBSE en anglès) un procés fonamental per a molts pacients. Malgrat els avenços en les tècniques d’escaneig cutani, les eines per a realitzar TBSEs de forma automàtica no han rebut massa atenció. Per tant, hem dissenyat i construït un escàner corporal de cobertura total per adquirir imatges de la superfície de la pell utilitzant llum amb polarització creuada. A més, hem desenvolupat un algoritme pel mapeig automàtic de les PSLs i l’estimació dels canvis entre exploracions. Els tests inicials de l’escàner mostren que aquest pot ésser utilitzat satisfactòriament pel mapeig automàtic i el control de canvis temporal de múltiples lesions

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29

Rosander, Frida. "Detection of Pathological Lesions in High Resolution Retinal Images." Thesis, Linköpings universitet, Tekniska högskolan, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-93837.

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High resolution retinal cameras with adaptive optics makes it possible to image small structures in the eye, such as photoreceptors and nerve bres, as well as blood vessels. Adaptive optics was rst developed to reduce blur in stellar images, but has later been used to correct for ocular aberrations in order to achieved higher resolution in retinal images. The development of these high resolution retinal cameras gives new pos- sibilities, and this master thesis has as purpose to investigate two of those: In-vivo estimation of cone photoreceptor distribution, and automatic detection of pathological areas in retinal images, as well as registration of retinal images. It is desirable to explore this in order to put helpful research tools into the hands of retinal researchers. For in-vivo detection of photoreceptors and calculations of cone density, the rtx1 adaptive optics retinal camera by Imagine Eyes together with the in house software AOdetect, were used. Comparison with previously published cone den- sity data showed that the in-vivo detection of photoreceptors gives an estimation of the cone densities at retinal eccentricities between 2.5 and 10 degrees. Detection of the pathological areas was performed in geographic atrophy images using an active snake contour method. It was stated that active snakes perform well, considering the diculties provided by the specic image features. However, the method has some shortcomings and it is suggested that alternative segmentation methods of atrophic areas in retinal images is further explored. In order to follow progression over time of the atrophy, an image registration algorithm has been implemented. Due to the characteristics of the geographic atrophy images, this algorithm is semi-automatic, that is, the user indicates the pairs of feature points as input. The registration performs well when the user chooses control point pairs carefully.

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30

Kok-Wiles, Siewli. "Comparing mammogram pairs in the detection of mammographic lesions." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.298421.

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31

Al-Juboori, Jamal Noori Ahmed. "Clinical study of the use of Photodynamic Detection (PDD) in assessing suspicious oral lesions." Thesis, University of Dundee, 2011. https://discovery.dundee.ac.uk/en/studentTheses/f113a48b-1b4d-48a3-8e9d-df19badd5f0d.

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Photodynamic Detection (PDD) is a diagnostic technique involving administration of a photosensitizer to the targeted cells that can be stimulated by short wavelength light which then leads to emission of light at a different wavelength (lower energy). The light emitted by the cells can be detected and analysed (by a spectroscope). All cells have the innate ability (due to endogenous fluorophores) to fluoresce, termed autofluorescence. Any cellular, metabolic or structural changes can alter the fluorescence intensity peaks. In this study 5-aminolevulinic acid (5-ALA) photosensitizer prodrug was used, which is metabolised in highly active cells to protoporphyrin IX (PpIX). Excitation of a cell at 405nm wavelength (light) leads to emission of autofluorescence at 500nm and PpIX at 635nm. The purpose of this investigation was to evaluate the use of compact spectroscopy together with the photosensitizer prodrug 5-ALA, in assessing clinically suspicious oral lesions. To that end the followings were assessed: • The fluorescence intensity ratio (FIR) or Red/Green ratio at 635/500nm measurements of normal anatomical sites at ten oral anatomical sites to map and create baseline readings for normal oral mucosal site fluorescence. • The effect of participants’ characteristics on the normal oral mucosal site FIR measurements. • The use the fluorescence intensity ratio (FIR) measurements to determine any differences between the lesion and the normal oral readings and whether the FIR from clinically suspicious oral lesions is associated with the histopathology grade. In addition to the sensitivity and specificity of the technique in assessing clinically suspicious (premalignant) oral lesions for potential malignant change.Prior to the trial commencing, approval were obtained from the Research Ethics Committee (REC), local NHS Research and Development (R&D), and Medicine Healthcare product Regulatory Agency (MHRA) and the University of Dundee Research Innovation Services (RIS). A total of thirty five participants with clinically suspicious oral lesions were recruited in Dundee (Dundee Dental Hospital) and Glasgow (Southern General Hospital). A Photodynamic Detection method using compact fluorescence spectroscopy and 5-ALA mouth rinse was applied. FIR measurements from ten normal anatomical sites were obtained in every patient to study the variation at different normal oral sites and the effect of the participant’s characteristics on these readings. In addition, two FIR measurements were obtained from each lesion and a further one taken from normal looking mucosa well beyond the lesion boundary (i.e. more than 5mm away) prior to biopsy. The readings were compared to study the reliability, reproducibility and efficacy of the photodynamic method in detecting mucosal abnormality. A total of 292 spectral readings obtained from normal mucosa were used to study the FIR measurements at normal oral anatomical sites. The results showed that the oral regions could be grouped into two broad categories with similar readings, the palatal and tongue readings in one group and buccal, ventral tongue, floor of the mouth, gingiva and lip mucosa on the other (essentially keratinized and non keratinized groups). The same set of readings were further analysed to study the effect of individual characteristics (age, gender, presence of oral prosthesis, metabolic diseases, smoking and alcohol consumptions) on the FIR measurements. There was no significant difference between FIR measurements within each of the groups studied, although at times sample sizes were very small.A total of 134 spectral readings obtained from 47 lesions that were biopsied from 35 patients recruited to the trial were used for the next part of the study. There were 91 spectra obtained from the lesions and 43 spectra obtained from the normal sites (more than 5mm away from the borders of the lesion) for comparison. There was a significant difference between the lesion and normal site readings. The FIR readings for the dysplastic lesions were significantly different when compared with the normal and benign hyperkeratoses. However there was no significant difference between dysplastic and inflammatory lesions (lichen planus, lichenoid lesions and candidal leukoplakia) on the one hand and inflammatory lesions and hyperkeratotic lesion on the other. Further analysis showed the sensitivity in detecting all the clinically suspicious oral lesions from the normal sites was 59.5% and specificity was 73.8%. The sensitivity in detecting dysplasia from normal sites was 100% and specificity 100%.Photodynamic Detection was able to detect a difference between the oral lesions from normal mucosa (but so is the naked eye!). However there was variation in the sensitivity and specificity in detecting a range of different pathological conditions. The technique was highly sensitive in detecting dysplasia from normal mucosa but unfortunately the technique is not able to discriminate reliably between dysplasia and inflammatory lesions whose clinical appearance can be very similar. In conclusion, the photodynamic detection method used in this study would not appear to offer a reliable screening tool for the early detection of oral dysplasia/cancer. The need to consider adjunctive tests that discriminate inflammation from dysplasia is required. Photodynamic Detection (PDD) is a diagnostic technique involving administration of a photosensitizer to the targeted cells that can be stimulated by short wavelength light which then leads to emission of light at a different wavelength (lower energy). The light emitted by the cells can be detected and analysed (by a spectroscope). All cells have the innate ability (due to endogenous fluorophores) to fluoresce, termed autofluorescence. Any cellular, metabolic or structural changes can alter the fluorescence intensity peaks. In this study 5-aminolevulinic acid (5-ALA) photosensitizer prodrug was used, which is metabolised in highly active cells to protoporphyrin IX (PpIX). Excitation of a cell at 405nm wavelength (light) leads to emission of autofluorescence at 500nm and PpIX at 635nm. The purpose of this investigation was to evaluate the use of compact fluorescence spectroscopy together with the photosensitizer prodrug 5-ALA, in assessing clinically suspicious oral lesions. To that end the followings were assessed: • The fluorescence intensity ratio (FIR) or Red/Green ratio at 635/500nm measurements of normal anatomical sites at ten oral anatomical sites to map and create baseline readings for normal oral mucosal site fluorescence. • The effect of participants’ characteristics on the normal oral mucosal site FIR measurements. • The use the fluorescence intensity ratio (FIR) measurements to determine any differences between the lesion and the normal oral readings and whether the FIR from clinically suspicious oral lesions is associated with the histopathology grade. In addition to the sensitivity and specificity of the technique in assessing clinically suspicious (premalignant) oral lesions for potential malignant change. Prior to the trial commencing, approval were obtained from the Research Ethics Committee (REC), local NHS Research and Development (R&D), and Medicine Healthcare product Regulatory Agency (MHRA) and the University of Dundee Research Innovation Services (RIS). A total of thirty five participants with clinically suspicious oral lesions were recruited in Dundee (Dundee Dental Hospital) and Glasgow (Southern General Hospital). A Photodynamic Detection method using compact fluorescence spectroscopy and 5-ALA mouth rinse was applied. FIR measurements from ten normal anatomical sites were obtained in every patient to study the variation at different normal oral sites and the effect of the participant’s characteristics on these readings.

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32

Melki, Imen. "Towards an automated framework for coronary lesions detection and quantification in cardiac CT angiography." Thesis, Paris Est, 2015. http://www.theses.fr/2015PESC1022/document.

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Les maladies coronariennes constituent l'ensemble des troubles affectant les artères coronaires. Elles sont la première cause mondiale de mortalité. Par conséquent, la détection précoce de ces maladies en utilisant des techniques peu invasives fournit un meilleur résultat thérapeutique, et permet de réduire les coûts et les risques liés à une approche interventionniste. Des études récentes ont montré que la tomodensitométrie peut être utilisée comme une alternative non invasive et fiable pour localiser et quantifier ces lésions. Cependant, l'analyse de ces examens, basée sur l'inspection des sections du vaisseau, reste une tâche longue et fastidieuse. Une haute précision est nécessaire, et donc seulement les cliniciens hautement expérimentés sont en mesure d'analyser et d'interpréter de telles données pour établir un diagnostic. Les outils informatiques sont essentiels pour réduire les temps de traitement et assurer la qualité du diagnostic. L'objectif de cette thèse est de fournir des outils automatisés de traitement d'angiographie CT, pour la visualisation et l'analyse des artères coronaires d'une manière non invasive. Ces outils permettent aux pathologistes de diagnostiquer et évaluer efficacement les risques associés aux maladies cardio-vasculaires tout en améliorant la qualité de l'évaluation d'un niveau purement qualitatif à un niveau quantitatif. Le premier objectif de ce travail est de concevoir, analyser et valider un ensemble d'algorithmes automatisés utiles pour la détection et la quantification de sténoses des artères coronaires. Nous proposons un nombre de techniques couvrant les différentes étapes de la chaîne de traitement vers une analyse entièrement automatisée des artères coronaires. Premièrement, nous présentons un algorithme dédié à l'extraction du cœur. L'approche extrait le cœur comme un seul objet, qui peut être utilisé comme un masque d'entrée pour l'extraction automatisée des coronaires. Ce travail élimine l'étape longue et fastidieuse de la segmentation manuelle du cœur et offre rapidement une vue claire des coronaires. Cette approche utilise un modèle géométrique du cœur ajusté aux données de l'image. La validation de l'approche sur un ensemble de 133 examens montre l'efficacité et la précision de cette approche. Deuxièmement, nous nous sommes intéressés au problème de la segmentation des coronaires. Dans ce contexte, nous avons conçu une nouvelle approche pour l'extraction de ces vaisseaux, qui combine ouvertures par chemin robustes et filtrage sur l'arbre des composantes connexes. L'approche a montré des résultats prometteurs sur un ensemble de 11 examens CT. Pour une détection et quantification robuste de la sténose, une segmentation précise de la lumière du vaisseau est cruciale. Par conséquent, nous avons consacré une partie de notre travail à l'amélioration de l'étape de segmentation de la lumière, basée sur des statistiques propres au vaisseau. La validation avec l'outil d'évaluation en ligne du challenge de Rotterdam sur la segmentation des coronaires, a montré que cette approche présente les mêmes performances que les techniques de l'état de l'art. Enfin, le cœur de cette thèse est consacré à la problématique de la détection et la quantification des sténoses. Deux approches sont conçues et évaluées en utilisant l'outil d'évaluation en ligne de l'équipe de Rotterdam. La première approche se base sur l'utilisation de la segmentation de la lumière avec des caractéristiques géométriques et d'intensité pour extraire les sténoses coronaires. La seconde utilise une approche basée sur l'apprentissage. Durant cette thèse, un prototype pour l'analyse automatisée des artères coronaires et la détection et quantification des sténoses a été développé. L'évaluation qualitative et quantitative sur différents bases d'examens cardiaques montre qu'il atteint le niveau de performances requis pour une utilisation clinique
Coronary heart diseases are the group of disorders that affect the coronary artery vessels. They are the world's leading cause of mortality. Therefore, early detection of these diseases using less invasive techniques provides better therapeutic outcome, as well as reduces costs and risks, compared to an interventionist approach. Recent studies showed that X-ray computed tomography (CT) may be used as an alternative to accurately locate and grade heart lesions in a non invasive way. However, analysis of cardiac CT exam for coronaries lesions inspection remains a tedious and time consuming task, as it is based on the manual analysis of the vessel cross sections. High accuracy is required, and thus only highly experienced clinicians are able to analyze and interpret the data for diagnosis. Computerized tools are critical to reduce processing time and ensure quality of diagnostics. The goal of this thesis is to provide automated coronaries analysis tools to help in non-invasive CT angiography examination. Such tools allow pathologists to efficiently diagnose and evaluate risks associated with CVDs, and to raise the quality of the assessment from a purely qualitative level to a quantitative level. The first objective of our work is to design, analyze and validate a set of automated algorithms for coronary arteries analysis with the final purpose of automated stenoses detection and quantification. We propose different algorithms covering different processing steps towards a fully automated analysis of the coronary arteries. Our contribution covers the three major blocks of the whole processing chain and deals with different image processing fields. First, we present an algorithm dedicated to heart volume extraction. The approach extracts the heart as one single object that can be used as an input masque for automated coronary arteries segmentation. This work eliminates the tedious and time consuming step of manual removing obscuring structures around the heart (lungs, ribs, sternum, liver...) and quickly provides a clear and well defined view of the coronaries. This approach uses a geometric model of the heart that is fitted and adapted to the image data. Quantitative and qualitative analysis of results obtained on a 114 exam database shows the efficiency and the accuracy of this approach. Second, we were interested to the problem of coronary arteries enhancement and segmentation. In this context, we first designed a novel approach for coronaries enhancement that combines robust path openings and component tree filtering. The approach showed promising results on a set of 11 CT exam compared to a Hessian based approach. For a robust stenoses detection and quantification, a precise and accurate lumen segmentation is crucial. Therefore, we have dedicated a part of our work to the improvement of lumen segmentation step based on vessel statistics. Validation on the Rotterdam Coronary Challenge showed that this approach provides state of the art performances. Finally, the major core of this thesis is dedicated to the issue of stenosis detection and quantification. Two different approaches are designed and evaluated using the Rotterdam online evaluation framework. The first approach get uses of the lumen segmentation with some geometric and intensity features to extract the coronary stenosis. The second is using a learning based approach for stenosis detection and stenosis. The second approach outperforms some of the state of the art works with reference to some metrics. This thesis results in a prototype for automated coronary arteries analysis and stenosis detection and quantification that meets the level of required performances for a clinical use. The prototype was qualitatively and quantitatively validated on different sets of cardiac CT exams

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33

Al-Hinnawi, Abdel-Razzak. "Computer aided detection of clustered micro-calcifications in the digitised mammogram." Thesis, University of Aberdeen, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301076.

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The presence of distributed micro-calcifications can be an indicator of early breast cancer. On the mammogram, they appear as bright smooth particles superimposed on the normal breast image background. Radiologists determine the occurrence of this lesion by detecting the individual micro-calcifications and then examining their distribution within the breast tissue. Due to the visual complexity of the mammogram, the detection sensitivity is usually less than 100%. The digital environment has the potential to increase the radiologist's accuracy. We have developed a computer aided detection (CAD) scheme that can identify clinically indicative clusters of micro-calcifications. The CAD algorithm emulates some aspects of the radiologists' approach by using contrast texture energy segmentation and morphological distribution analysis. On a local database of 61 mammograms digitised at 100μm with 8 bits intensity resolution, the CAD returns: a) 85% sensitivity (91% for malignant lesions and 78% for those that are benign), b) 0.33 false positive clusters (FPC) per image and c) 92% specificity. Therefore, the output from the CAD is shown to compare favourably with the performance of an expert radiologist. It also compares favourably with other CAD techniques, exceeding many algorithms which employ a higher level of mathematical complexity. The scheme is tested on an international database provided by the Mammographic Image Analysis Society. In this case it returns a) 96.4% sensitivity (100% for malignant lesions and 92% for those that are benign) b) 2.35 FPC rate per image and c) 33% specificity. The higher FPC rate is attributed to the different acquisition and production of the digital mammograms. It is concluded that this can be reduced by employing a shape analysis procedure to the CAD's final output. It is shown that the image processing principles we have implemented are generally successful on databases which are produced at other centres under different technical conditions.

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34

Lemaire, Olivier. "Contribution a l'etude des proprietes biologiques des rna du virus de la rhizomanie (beet necrotic yellow vein virus) et de leur role dans l'etiologie de la maladie." Université Louis Pasteur (Strasbourg) (1971-2008), 1988. http://www.theses.fr/1988STR13115.

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35

Bovis, Keir Jonathan. "An adaptive knowledge-based model for detecting masses in screening mammograms." Thesis, University of Exeter, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.269735.

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36

Zuluaga, Valencia Maria Alejandra. "Methods for automation of vascular lesions detection in computed tomography images." Thesis, Lyon 1, 2011. http://www.theses.fr/2011LYO10010/document.

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Les travaux de cette thèse sont consacrés à la détection et le diagnostic des lésions vasculaires, particulièrement dans le cas la maladie coronaire. La maladie coronaire continue à être la première cause de mortalité dans les pays industrialisés. En général, l'identification des lésions vasculaires est abordée en essayant de modéliser les anormalités (lésions). Le principal inconvénient de cette approche est que les lésions sont très hétérogènes, ce qui rend difficile la détection de nouvelles lésions qui n'ont pas été prises en compte par le modèle. Dans cette thèse, nous proposons de ne pas modéliser directement les lésions, mais de supposer que les lésions sont des événements anormaux qui se manifestent comme points avec une faible densité de probabilité. Nous proposons l'utilisation de deux méthodes de classification basées sur les Machines à Vecteurs de Support (SVM) pour résoudre le problème de détection du niveau de densité. Le principal avantage de ces deux méthodes est que la phase d'apprentissage ne requiert pas de données étiquetées représentant les lésions. La première méthode est complètement non supervisée, alors que la seconde exige des étiquettes seulement pour les cas qu'on appelle sains ou normaux. L'utilisation des algorithmes de classification sélectionnés nécessite des descripteurs tels que les anomalies soient représentées comme des points avec une densité de probabilité faible. A cette fin, nous avons développé une métrique basée sur l'intensité de l'image, que nous avons appelée concentric rings. Cette métrique est sensible à la quasi-symétrie des profils d'intensité des vaisseaux sains, mais aussi aux écarts par rapport à cette symétrie, observés dans des cas pathologiques. De plus, nous avons sélectionné plusieurs autres descripteurs candidats à utiliser comme entrée pour les classifieurs. Des expériences sur des données synthétiques et des données de CT cardiaques démontrent que notre métrique a une bonne performance dans la détection d'anomalies, lorsqu'elle est utilisée avec les classifeurs retenus. Une combinaison de plusieurs descripteurs candidats avec la métrique concentric rings peut améliorer la performance de la détection. Nous avons défini un schéma non supervisé de sélection de descripteurs qui permet de déterminer un sous-ensemble optimal de descripteurs. Nous avons confronté les résultats de détection réalisée en utilisant le sous-ensemble de descripteurs sélectionné par notre méthode avec les performances obtenues avec des sous-ensembles sélectionnés par des méthodes supervisées existantes. Ces expériences montrent qu'une combinaison de descripteurs bien choisis améliore effectivement les performances des classifieurs et que les meilleurs résultats s'obtiennent avec le sous-ensemble sélectionné par notre méthode, en association avec les algorithmes de détection retenus. Finalement, nous proposons de réaliser un recalage local entre deux images représentant différentes phases du cycle cardiaque, afin de confronter les résultats de détection dans ces images (phases). L'objectif ici est non seulement d'attirer l'attention du praticien sur les anomalies détectées comme lésions potentielles, mais aussi de l'aider à conforter son diagnostic en visualisant automatiquement la même région reconstruite à différents instants du cycle cardiaque
This thesis presents a framework for the detection and diagnosis of vascular lesions with a special emphasis on coronary heart disease. Coronary heart disease remains to be the first cause of mortality worldwide. Typically, the problem of vascular lesion identification has been solved by trying to model the abnormalities (lesions). The main drawback of this approach is that lesions are highly heterogeneous, which makes the detection of previously unseen abnormalities difficult. We have selected not to model lesions directly, but to treat them as anomalies which are seen as low probability density points. We propose the use of two classification frameworks based on support vector machines (SVM) for the density level detection problem. The main advantage of these two methods is that the learning stage does not require labeled data representing lesions, which is always difficult to obtain. The first method is completely unsupervised, whereas the second one only requires a limited number of labels for normality. The use of these anomaly detection algorithms requires the use of features such that anomalies are represented as points with low probability density. For this purpose, we developed an intensity based metric, denoted concentric rings, designed to capture the nearly symmetric intensity profiles of healthy vessels, as well as discrepancies with respect to the normal behavior. Moreover, we have selected a large set of alternative candidate features to use as input for the classifiers. Experiments on synthetic data and cardiac CT data demonstrated that our metric has a good performance in the detection of anomalies, when used with the selected classifiers. Combination of other features with the concentric rings metric has potential to improve the classification performance. We defined an unsupervised feature selection scheme that allows the definition of an optimal subset of features. We compared it with existent supervised feature selection methods. These experiments showed that, in general, the combination of features improves the classifiers performance, and that the best results are achieved with the combination selected by our scheme, associated with the proposed anomaly detection algorithms. Finally, we propose to use image registration in order to compare the classification results at different cardiac phases. The objective here is to match the regions detected as anomalous in different time-frames. In this way, more than attract the physician's attention to the anomaly detected as potential lesion, we want to aid in validating the diagnosis by automatically displaying the same suspected region reconstructed in different time-frames

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37

Tam, Darlene Melody. "Patient experiences with high-risk oral lesions from detection to diagnosis." Thesis, University of British Columbia, 2014. http://hdl.handle.net/2429/46322.

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With <50% 5-year survival rates, oral cancer is often diagnosed at late-stage. Detection of lesions at earlier stages results in better prognosis. Continued tobacco use after cancer treatment is a risk for recurrence. However, little is known of the process from lesion identification to diagnosis, impact of smoking behavioural changes and barriers for tobacco cessation in patients with a high-risk oral lesion (HRL) diagnosis. Two survey-type questionnaires were used to collect data on tobacco usage and patient experiences leading to HRL diagnosis. Patients attending the BC Cancer Agency diagnosed with HRLs within 12 months of interview were invited to participate in Study I. Patients who also smoked ???100 cigarettes within 5 years of questionnaire were eligible for Study II. Among 150 Study I patients, 61% were self-identified (SIG) and 39% were professionally screened (PSG; 88% by dental professionals). PSG identified significantly more precancerous lesions compared with SIG (54% vs. 23%, P = 0.0003) and was more likely to screen ever smokers (P = 0.05). SIG showed significantly higher rates of time delay from detection to diagnosis ???3 months, compared with PSG (58% vs. 36%, P = 0.007). Frequency of dental visits strongly correlated with identification of HRLs by healthcare professionals (P = 0.004). Common symptoms for SIG were pain (77%) and non-healing ulcers (62%), which prompted patients to seek healthcare, while 71% of PSG patients were asymptomatic. Surprisingly, almost half (47%) of patients were not aware of oral cancer. Of 58 Study II patients, main tobacco cessation barriers included stress (67%), smoking enjoyment (64%), and not being ready to quit (60%). Increased nicotine intake may be associated with increased difficulties with tobacco cessation. Knowledge of health risks from tobacco usage (71%) and tobacco cessation aids (43%) were main cessation facilitators. The HRL diagnosis was a pivotal factor for patients to stop or reduce cigarette consumption. Oral cancer screening by healthcare professionals, particularly dental professionals, can facilitate identification of earlier oral lesions at risk of cancer progression. Promotion of oral cancer awareness and tobacco cessation for patients and healthcare professionals may facilitate earlier diagnosis of oral lesions and prevent recurrent lesions.

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38

GIRARDIN, TORDEUR CATHERINE. "Detection des papillomavirus humains dans les lesions du tractus ano-genital." Reims, 1990. http://www.theses.fr/1990REIMM064.

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39

Sutton, Kate Marie. "Mutation detection in normal mucosa and early lesions of colorectal cancer." Thesis, University of Leeds, 2014. http://etheses.whiterose.ac.uk/6410/.

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Colorectal cancer (CRC) has a relatively poor prognosis when detected at a later stage, therefore understanding its development to allow prevention or early detection is key to improving outcomes. Bowel cancer screening allows for the detection of tumours and precursor lesions. Even earlier changes could potentially be detected; genetic aberrations within the normal bowel before phenotypic changes occur. Early lesions may develop independently throughout the bowel or through a cancer field effect. FAP adenomas are a useful model due to adenomas developing within the same environment and all incurring the same first “hit” within APC. Using next generation sequencing the genetic profiles of FAP adenomas can be compared to better understand the development of these lesions. Firstly the sensitivity of pyrosequencing and NGS was determined as was the value of PCR-based mutant enrichment techniques. Samples of carcinoma, adenoma and their associated normals alongside normal mucosa from patients with normal colonoscopies were tested for mutations in commonly mutated genes in CRC using NGS. Multiple FAP adenomas from four patients were also tested with this mutation panel. Alongside this, copy number analysis was performed. The mutational and copy number data was used to ascertain the pattern of adenoma development throughout the bowel. Mutations were detected in carcinoma associated normal in APC, KRAS, CTNNB1 and SMAD4. The KRAS mutations in carcinoma associated normal differed to the KRAS mutation in the matched tumour. No mutations were detected in oncogenes in adenoma associated normal or normal mucosa from patients with normal colonoscopies. Studying mutations and copy number aberrations in FAP adenomas revealed that some adenomas shared specific lesions, indicating that they were clonally related. These results have confirmed previously findings of KRAS mutations in carcinoma associated normal mucosa as well as describing mutations in APC, CTNNB1 and SMAD4. This combined with the large amount of similarity in terms of mutations and copy number seen in adenomas from the same patient provides evidence for the cancer field theory.

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40

Bluestein, Katharine T. "Inversion Recovery Sequences for the Detection of Cortical Lesions in Multiple Sclerosis Using a 7 Tesla MR Imaging System." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1337362094.

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41

Bélanger, Marie-José 1967. "Requirements for the detection of atherosclerosis lesions in carotid arteries with SPECT." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/9028.

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Анотація:

Thesis (Ph.D.)--Harvard--Massachusetts Institute of Technology Division of Health Sciences and Technology, 2000.
Includes bibliographical references (leaves 135-139).
Detecting the metabolic state of atherosclerotic lesions is a promise of nuclear medicine imaging. Several researchers are developing radiopharmaceuticals for atherosclerosis imaging. In this thesis, we provided procedural guidelines to detect carotid lesions with single photon ,Emission Computed Tomography (SPECT). We first established a method to assess the requirements for "successful" lesion detection. Although this method was used to detect focal carotid lesions, it is also applicable to the detection of focal lesions in other arteries or veins. We measured lesion detectability using the output values of a 3D moving Non Pre-Whitening Matched Filter (30 mNPWMF) with the .Localization Receiver Operating Characteristics (LROC) paradigm. We simulated SPECT images of the neck using SimSPECT, our in-house analog Monte Carlo radiation transport code. We used 400 64x64 reconstructed images formed by 99mTc photons of a focal lesion in a carotid artery next to a jugular vein, both in a cylindrical water neck. We then applied the 3D \ mNPWMF along the large neck vessels. The NPWMF has been found to correlate well with human observers in simple ROC studies. We expect the mNPWMF operation to mimic a radiologist who already has a blood pool image which identifies the location of the large neck vessels. Using this detection method, we calculated that 1 to 6 kBq/cm were needed in the lesion. At large blood activity (4.6 times the surrounding tissue activity), the minimum radiopharmaceutical uptake increased by 1.6-2.9 times when the patient was lying down as opposed to sitting up. At this blood activity, a carotid dilation of 1 cm radius distracted the moving Matched Filter from lesion detection. We recommend that the blood activity be as low as possible to avoid any focal dilation from distracting our detector. We recommend that, at high blood activity, the patient be imaged in an upright position in which the jugular veins are collapsed, preventing their blood pool activity from obscuring the carotid arteries. Finally, we showed that a lesion needed 140% of the radiopharmaceutical when acquired with a radius of rotation (ROR) of25 cm instead of 15 cm. In conclusion, we assessed successfully the effect of the jugular veins and carotid dilation on detection of carotid lesions in SPECT images of the neck using the LROC detection paradigm.
by Marie-José Bélanger.
Ph.D.

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42

Simpson, Inga Caroline. "Lesbian detective fiction : the outsider within." Thesis, Queensland University of Technology, 2008. https://eprints.qut.edu.au/20120/1/Inga_Simpson_Exegesis.pdf.

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Lesbian Detective Fiction: the outsider within is a creative writing thesis in two parts: a draft lesbian detective novel, titled Fatal Development (75%) and an exegesis containing a critical appraisal of the sub-genre of lesbian detective fiction, and of my own writing process (25%). Creative work: Fatal Development -- It wasn’t the first time I’d seen a dead body, but it didn’t seem to get any easier. -- When Dirk and Stacey discover a body in the courtyard of their Brisbane woolstore apartment, it is close friend and neighbour, Kersten Heller, they turn to for support. The police assume Stuart’s death was an accident, but when it emerges that he was about to take legal action against the woolstore’s developers, Bovine, Kersten decides there must be more to it. Her own apartment has flooded twice in a month and the builders are still in and out repairing defects. She discovers Stuart was not alone on the roof when he fell to his death and the evidence he had collected for his case against Bovine has gone missing. Armed with this knowledge, and fed up with the developer’s ongoing resistance to addressing the building’s structural issues, Kersten organises a class action against Bovine. Kersten draws on her past training as a spy to investigate Stuart’s death, hiding her activities, and details of her past, from her partner, Toni. Her actions bring her under increasing threat as her apartment is defaced, searched and bugged, and she is involved in a car chase across New Farm. Forced to fall back on old skills, old habits and memories return to the surface. When Toni discovers that Kersten has broken her promise to leave the investigation to the police, she walks out. The neighbouring – and heritage-listed – Riverside Coal development site burns to the ground, and Kersten and Dirk uncover evidence of a network of corruption involving developers and local government officials. After she is kidnapped in broad daylight, narrowly escaping from the boot of a moving car, Kersten is confident she is right, but with Toni not returning her calls, and many of the other residents selling up, including Dirk and Stacey, Kersten begins to question her judgment. In a desperate attempt to turn things around, Kersten calls on an old Agency contact to help prove Bovine was involved in Stuart’s death, her kidnapping, and ongoing corruption. To get the evidence she needs, Kersten plays a dangerous game: letting Bovine know she has uncovered their illegal operations in order to draw them into revealing themselves on tape. Hiding alone in a hotel room, Kersten is finally forced to confront her past: When Mirin didn’t come home that night, I was ready to go out and find her myself, disappear, and start a new life together somewhere far away. Instead they pulled me in before I could finish making arrangements, questioned me for hours, turned everything around. It was golden child to problem child in the space of a day. This time, she’s determined, things will turn out differently. Exegesis: The exegesis traces the development of lesbian detective fiction, including its dual origins in detective and lesbian fiction, to compare the current state of the sub-genre with the early texts and to establish the dominant themes and tropes. I focus particularly on Australian examples of the sub-genre, examining in detail Claire McNab’s Denise Cleever series and Jan McKemmish’s A Gap in the Records, in order to position my own lesbian detective novel between these two works. In drafting Fatal Development, I have attempted to include some of the political content and complexity of McKemmish’s work, but with a plot-driven narrative. I examine the dominant tropes and conventions of the sub-genre, such as: lesbian politics; the nature of the crime; method of investigation; sex and romance; and setting. In the final section, I explain the ways in which I have worked within and against the subgenre’s conventions in drafting a contemporary lesbian detective novel: drawing on tradition and subverting reader expectations. Throughout the thesis, I explore in detail the tradition of the fictional lesbian detective as an outsider on the margins of society, disrupting notions of power and gender. While the lesbian detective’s outsider status grants her moral agency and the capacity to achieve justice and generate change, she is never fully accepted. The lesbian detective remains an outsider within. For the lesbian detective, working within a system that ultimately discriminates against her involves conflict and compromise, and a sense of double-play in being part of two worlds but belonging to neither. I explore how this double-consciousness can be applied to the lesbian writer in choosing whether to write for a mainstream or lesbian audience.

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43

Simpson, Inga Caroline. "Lesbian detective fiction : the outsider within." Queensland University of Technology, 2008. http://eprints.qut.edu.au/20120/.

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Анотація:

Lesbian Detective Fiction: the outsider within is a creative writing thesis in two parts: a draft lesbian detective novel, titled Fatal Development (75%) and an exegesis containing a critical appraisal of the sub-genre of lesbian detective fiction, and of my own writing process (25%). Creative work: Fatal Development -- It wasn’t the first time I’d seen a dead body, but it didn’t seem to get any easier. -- When Dirk and Stacey discover a body in the courtyard of their Brisbane woolstore apartment, it is close friend and neighbour, Kersten Heller, they turn to for support. The police assume Stuart’s death was an accident, but when it emerges that he was about to take legal action against the woolstore’s developers, Bovine, Kersten decides there must be more to it. Her own apartment has flooded twice in a month and the builders are still in and out repairing defects. She discovers Stuart was not alone on the roof when he fell to his death and the evidence he had collected for his case against Bovine has gone missing. Armed with this knowledge, and fed up with the developer’s ongoing resistance to addressing the building’s structural issues, Kersten organises a class action against Bovine. Kersten draws on her past training as a spy to investigate Stuart’s death, hiding her activities, and details of her past, from her partner, Toni. Her actions bring her under increasing threat as her apartment is defaced, searched and bugged, and she is involved in a car chase across New Farm. Forced to fall back on old skills, old habits and memories return to the surface. When Toni discovers that Kersten has broken her promise to leave the investigation to the police, she walks out. The neighbouring – and heritage-listed – Riverside Coal development site burns to the ground, and Kersten and Dirk uncover evidence of a network of corruption involving developers and local government officials. After she is kidnapped in broad daylight, narrowly escaping from the boot of a moving car, Kersten is confident she is right, but with Toni not returning her calls, and many of the other residents selling up, including Dirk and Stacey, Kersten begins to question her judgment. In a desperate attempt to turn things around, Kersten calls on an old Agency contact to help prove Bovine was involved in Stuart’s death, her kidnapping, and ongoing corruption. To get the evidence she needs, Kersten plays a dangerous game: letting Bovine know she has uncovered their illegal operations in order to draw them into revealing themselves on tape. Hiding alone in a hotel room, Kersten is finally forced to confront her past: When Mirin didn’t come home that night, I was ready to go out and find her myself, disappear, and start a new life together somewhere far away. Instead they pulled me in before I could finish making arrangements, questioned me for hours, turned everything around. It was golden child to problem child in the space of a day. This time, she’s determined, things will turn out differently. Exegesis: The exegesis traces the development of lesbian detective fiction, including its dual origins in detective and lesbian fiction, to compare the current state of the sub-genre with the early texts and to establish the dominant themes and tropes. I focus particularly on Australian examples of the sub-genre, examining in detail Claire McNab’s Denise Cleever series and Jan McKemmish’s A Gap in the Records, in order to position my own lesbian detective novel between these two works. In drafting Fatal Development, I have attempted to include some of the political content and complexity of McKemmish’s work, but with a plot-driven narrative. I examine the dominant tropes and conventions of the sub-genre, such as: lesbian politics; the nature of the crime; method of investigation; sex and romance; and setting. In the final section, I explain the ways in which I have worked within and against the subgenre’s conventions in drafting a contemporary lesbian detective novel: drawing on tradition and subverting reader expectations. Throughout the thesis, I explore in detail the tradition of the fictional lesbian detective as an outsider on the margins of society, disrupting notions of power and gender. While the lesbian detective’s outsider status grants her moral agency and the capacity to achieve justice and generate change, she is never fully accepted. The lesbian detective remains an outsider within. For the lesbian detective, working within a system that ultimately discriminates against her involves conflict and compromise, and a sense of double-play in being part of two worlds but belonging to neither. I explore how this double-consciousness can be applied to the lesbian writer in choosing whether to write for a mainstream or lesbian audience.

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44

Ganiler, Onur. "Automated detection of new multiple sclerosis lesions in longitudinal brain magnetic resonance imaging." Doctoral thesis, Universitat de Girona, 2014. http://hdl.handle.net/10803/283552.

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This thesis deals with the detection of new multiple sclerosis (MS) lesions in longitudinal brain magnetic resonance (MR) imaging. This disease is characterized by the presence of lesions in the brain, predominantly in the white matter (WM) tissue of the brain. The detection and quantification of new lesions are crucial to follow-up MS patients. Moreover, the manual detection of these new lesions is not only time-consuming, but is also prone to intra- and inter-observer variability. Therefore, the development of automated techniques for the detection MS lesions is a major challenge
Aquesta tesi es centra en la detecció automàtica de lesions noves d'esclerosi múltiple (EM) en estudis longitudinals del cervell mitjançant l'ús d'imatges de ressonància magnètica (RM). Aquesta malaltia es caracteritza per la presència de lesions al cervell, predominantment en el teixit de la matèria blanca, i la detecció i la quantificació de les noves lesions són elements crucials per al seguiment dels pacients. No obstant això, la detecció manual d'aquestes noves lesions no només requereix de molt temps, sinó que també és propensa a la variabilitat intra- i inter-observador. Cal tenir en compte que les lesions d'EM són molt petites en comparació amb tot el cervell. Per tant, el desenvolupament de tècniques automàtiques per a la detecció de lesions d'EM és un gran repte

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45

Hannila, I. (Ilkka). "T2 relaxation of articular cartilage:normal variation, repeatability and detection of patellar cartilage lesions." Doctoral thesis, Oulun yliopisto, 2016. http://urn.fi/urn:isbn:9789526212043.

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Abstract Cartilage-related diseases such as osteoarthritis (OA) are a major cause of disability and decrease in the quality of life. Moreover OA causes a heavy economical burden on the social welfare and health care systems. Conventional magnetic resonance imaging (MRI) provides accurate noninvasive method of morphological evaluation of the articular cartilage. However, there are early degenerative changes in the articular cartilage that can be evaluated with modern quantitative MRI methods prior to the signs of cartilage loss. In this study, T2 relaxation time of the articular cartilage was further evaluated in 1.5T in vivo using clinical patients and asymptomatic volunteers. The detection of focal patellar cartilage lesions in T2 mapping as compared to standard clinical MRI was evaluated. T2 mapping showed more lesions than the clinical MRI, and in T2 maps the lesions appeared generally wider. This suggests that T2-mapping is feasible in the clinical setting and may reveal cartilage lesions not seen in the standard knee MRI. The normal topographical variation of T2 relaxation time of articular cartilage in different compartments of the knee joint and at different zones of cartilage in young healthy adults was assessed. T2 values were significantly higher in the superficial zone as compared to the deep tissue at all locations and there was remarkable variation in T2 relaxation between different locations. The normal variation in cartilage T2 within a joint is significant and should be acknowledged when pathology-related T2 changes are investigated. The short- and long-term repeatability of T2 relaxation time measurements of articular cartilage in the knee joint was assessed. The results showed mostly good repeatability, and with careful patient positioning T2 relaxation time at the different cartilage surfaces of the knee can be accurately determined
Tiivistelmä Nivelrikko, joka usein liittyy nivelruston vaurioitumiseen, aiheuttaa merkittävää toimintakyvyn ja elämänlaadun heikentymistä ikääntyvässä väestössä. Lisäksi nivelrikosta aiheutuu merkittäviä kustannuksia sosiaali- ja terveydenhuollolle. Magneettikuvaus on tarkka kajoamaton menetelmä rustovaurioiden arvioimiseksi. Kuitenkin rustovaurion alkuvaiheessa tapahtuu ruston sisäisiä rakenteellisia ja biokemiallisia muutoksia, joita on mahdollista arvioida uusilla kvantitatiivisilla magneettikuvausmenetelmillä ennen varsinaisten rustopuutosten kehittymistä. Tässä tutkimuksessa tutkittiin ruston T2-relaksaatioaikamittausta 1.5T magneettikuvauslaitteella sekä potilasaineistossa että vapaaehtoisilla. Tutkimuksessa verrattiin paikallisten rustomuutosten havaitsemisen herk¬kyyttä T2-relaksaatioaikakartoituksen ja tavanomaisen kliinisen magneetti¬kuvauksen välillä kliinisessä potilasaineistossa. T2-relaksaatiomittaus osoitti useampia muutoksia kuin kliininen magneettikuvaus ja muutokset olivat yleensä laajempia. Voidaan olettaa, että T2-relaksaatioaikamittaus soveltuu kliiniseen käyttöön ja voi osoittaa tavanomaisessa magneettikuvauksessa näkymättömiä rustomuutoksia. Tutkimuksessa arvioitiin ruston T2-relaksaatioajan paikkakohtaista ja kerroksittaista vaihtelua polven nivelpintojen eri alueilla nuorten vapaaehtoisten aineistossa. T2-relaksaatioaika oli merkitsevästi pidempi ruston pinnallisessa kuin syvässä kerroksessa kaikilla nivelpintojen alueilla. Lisäksi T2-relaksaatioajassa oli merkittävää normaalia vaihtelua eri alueiden välillä ja tämä tulisi huomioida ruston patologisia muutoksia arvioitaessa. Tutkimuksessa arvioitiin polven ruston T2-relaksaatioajan lyhyen ja pitkän aikavälin toistettavuutta vapaaehtoisaineistossa. Tulokset osoittivat enimmäkseen hyvää toistettavuutta ja huolellisella asettelulla voidaan ruston T2-relaksaatioaika mitata luotettavasti polven nivelpintojen eri alueilla

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46

Rajab, Maher I. "Neural network edge detection and skin lesions image segmentation methods : analysis and evaluation." Thesis, University of Nottingham, 2003. http://eprints.nottingham.ac.uk/13681/.

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Similar to a human observer, an automated image vision system is able to recognise most parts of an object if the system could accurately trace and reflect its true shape. This has prompted the development of the many diverse edge detection techniques. Neural networks have been successfully applied to pattern recognition tasks and edge detection. However, there is a great necessity to analyse neural network models so as to achieve close insight into their internal functionality. To this purpose, a new and general training set, consisting of a limited number of prototype edge patterns, is proposed to analyse the problem of neural network edge detection. This thesis also proposes two approaches to the skin lesion image segmentation problem. The first is a mainly thresholding segmentation method where an optimal threshold is determined iteratively by an isodata algorithm. The second method proposed is based on neural network edge detection and a rational Gaussian curve that fits an approximate closed elastic curve between the recognized neural network edge patterns. A quantitative comparison of the techniques is enabled by the use of synthetic lesions to which Gaussian noise is added. The proposed techniques are also compared with an established automatic skin segmentation method. It is demonstrated that for lesions with a range of different border irregularity properties the thresholding segmentation method provides the best performance over a range of signal to noise ratios; the thresholding segmentation method is also demonstrated to have similar performance when tested on real skin lesions.

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47

Banerjee, A. K. "The detection, outcome and molecular biology of pre-invasive lesions of the bronchus." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1343628/.

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Introduction: It is proposed that squamous cell carcinoma of the bronchus develops from carcinogen-exposed epithelium through a series of pre-invasive lesions of increasing histological and cytological abnormality. This has not been reliably demonstrated, and it is not known whether pre-invasive lesion development follows a predictable time-course and pattern or whether all pre-invasive lesions are committed to the development of malignancy. Pre-invasive lesions manifest genetic changes similar in pattern to that of squamous cell carcinoma. The accumulation of genetic damage, as a consequence of prolonged carcinogen exposure, may drive the progression of an individual pre-invasive lesion to malignancy, and the ultimate pattern of genetic changes may determine the outcome of that lesion. Methods: In the present work patients with pre-invasive lesions underwent serial bronchoscopy and biopsy to determine the natural history of pre-invasive lesions. Serial biopsies from lesions under follow-up were studied histologically and using loss of heterozygosity analysis at chromosomal loci thought to be involved in the pathogenesis of squamous cell carcinoma. Results: The natural history of pre-invasive lesions is variable. Some lesions progress, some regress and some remain unchanged histologically. Different lesions in a single patient may have different natural histories and different outcomes. Short-term follow-up may misrepresent the long-term evolution of an individual lesion or bronchoscopic location. Molecular studies showed that different lesions in individual patients appeared to have originated from a single progenitor cell, but acquired significant genetic differences during lesion development. Progression of pre-invasive lesions to carcinoma was associated with loss of heterozygosity along the majority of 3p with loss at 9p and the acquisition of 4p16 loss at the transition from carcinoma-in-situ to invasive disease. Regression to normal epithelium was associated with the failure to acquire these changes at the same time.

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48

Mokhomo, Molise. "Automatic detection and segmentation of brain lesions from 3D MR and CT images." Master's thesis, University of Cape Town, 2014. http://hdl.handle.net/11427/9089.

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Includes bibliographical references.
The detection and segmentation of brain pathologies in medical images is a vital step which helps radiologists to diagnose a variety of brain abnormalities and set up a suitable treatment. A number of institutes such as iThemba LABS still rely on a manual identification of abnormalities. A manual identification is labour intensive and tedious due to the large amount of medical data to be processed and the presence of small lesions. This thesis discusses the possible methods that can be used to address the problem of brain abnormality segmentation in MR and CT images. The methods are general enough to segment different types of abnormalities. The first method is based on the symmetry of the brain while the second method is based on a brain atlas. The symmetry-based method assumes that healthy brain tissues are symmetrical in nature while abnormal tissues are asymmetric with respect to the symmetry plane dividing the brain into similar hemispheres. The three major steps involved in this approach are the symmetry detection, tilt correction and asymmetry quantification. The method used to determine the brain symmetry automatically is discussed and its accuracy has been validated against the ground-truth using mean angular error (MAE) and distance error (DE). Two asymmetric quantification methods are studied and validated on real and simulated patient’s T1- and T2-weighted MR images with low and highgrade gliomas using true positive volume fraction (TPVF), false positive volume fraction (FPVF) and false negative volume fraction (FNVF). The atlas-based method is also presented and relies on the assumption that abnormal brain tissues appear with intensity values different from those of the surrounding healthy tissues. To detect and segment brain lesions the test image is aligned onto the atlas space and voxel by voxel analysis is performed between the atlas and the registered image. This methods is also evaluated on the simulated T1-weighted patient dataset with simulated low and high grade gliomas. The atlas, containing prior knowledge of normal brain tissues, is built from a set of healthy subjects.

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49

Golde, Jonas, Florian Tetschke, Julia Walther, Tobias Rosenauer, Franz Hempel, Christian Hannig, Edmund Koch, and Lars Kirsten. "Detection of carious lesions utilizing depolarization imaging by polarization sensitive optical coherence tomography." SPIE, 2018. https://tud.qucosa.de/id/qucosa%3A71747.

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As dental caries is one of the most common diseases, the early and noninvasive detection of carious lesions plays an important role in public health care. Optical coherence tomography (OCT) with its ability of depth-resolved, high-resolution, noninvasive, fast imaging has been previously recognized as a promising tool in dentistry. Additionally, polarization sensitive imaging provides quantitative measures on the birefringent tissue properties and can be utilized for imaging dental tissue, especially enamel and dentin. By imaging three exemplary tooth samples ex vivo with proximal white spot, brown spot, and cavity, we show that the combination of polarization sensitive OCT and the degree of polarization uniformity (DOPU) algorithm is a promising approach for the detection of proximal carious lesions due to the depolarization contrast of demineralized tissue. Furthermore, we investigate different sizes of the DOPU evaluation kernel on the resulting contrast and conclude a suitable value for this application. We propose that DOPU provides an easy to interpret image representation and appropriate contrast for possible future screening applications in early caries diagnostics.

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50

Aljehani, Abdulaziz Saad. "Application of two fluorescence methods for detection and quantification of smooth surface carious lesions /." Stockholm, 2006. http://diss.kib.ki.se/2006/91-7140-793-6/.

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Дисертації з теми "Lesion detection"

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