Dissertations / Theses on the topic 'Lesions segmentation'
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Abdullah, Bassem A. "Segmentation of Multiple Sclerosis Lesions in Brain MRI." Scholarly Repository, 2012. http://scholarlyrepository.miami.edu/oa_dissertations/711.
Full textNaeslund, Elin. "Stroke Lesion Segmentation for tDCS." Thesis, Linköpings universitet, Medicinsk informatik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71472.
Full textWan, Fengkai. "Deep Learning Method used in Skin Lesions Segmentation and Classification." Thesis, KTH, Medicinsk teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-233467.
Full textCabezas, Grebol Mariano. "Atlas-based segmentation of multiple sclerosis lesions in magnetic resonance imaging." Doctoral thesis, Universitat de Girona, 2013. http://hdl.handle.net/10803/119608.
Full textAquesta tesi es centra en la segmentació de imatges de ressonància magnètica del cervell aplicada a pacients d'esclerosi múltiple. Aquesta malaltia es caracteritza per l'aparició de lesions de matèria blanca, visibles en aquesta modalitat d'imatge. Després d'un anàlisi exhaustiu de l'estat de l'art en aquest tòpic, remarcant la importància de la informació prèvia, i també de la segmentació basada en atles del cervell, proposem dues estratègies diferents per a la segmentació de lesions basades en les conclusions d'ambdós estudis. La primera proporciona una classificació inicial dels teixits mitjançant una extensió de l'algorisme d'esperança-maximització, que es refina posteriorment amb un procés de segmentació de les lesions basat en una binarització inicial i una conseqüent estratègia de reducció de falsos positius a nivell de regió. La segona proposta es focalitza bàsicament en la segmentació de lesions i utilitza una combinació de classificadors febles entrenats amb un ric conjunt de característiques que inclou imatges d'intensitat, mapes probabilístics provinents d'un atles, un mapa d'intensitats atípiques i informació contextual. Ambdues estratègies han estat provades amb una nova base de dades formada per imatges de tres hospitals diferents amb diferent càrrega lesional per cas. L'avaluació d'aquestes proves, que s'ha dut a terme de forma quantitativa i qualitativa, inclou una comparativa i utilitza diferents mètriques de detecció i segmentació. L'anàlisi d'aquests resultats apunta a un millor rendiment relatiu a l'estat de l'art actual, amb una millor detecció per part de la primera estratègia i una millor segmentació per part de la segona
Ma, Pu. "Automatic segmentation of multiple sclerosis lesions in magnetic resonance brain images." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp05/MQ63536.pdf.
Full textGarcía-Lorenzo, Daniel. "Robust Segmentation of Focal Lesions on Multi-Sequence MRI in Multiple Sclerosis." Phd thesis, Université Rennes 1, 2010. http://tel.archives-ouvertes.fr/tel-00485645.
Full textGarcía, Lorenzo Daniel. "Robust segmentation of focal lesions on multi-sequence MRI in multiple sclerosis." Rennes 1, 2010. http://www.theses.fr/2010REN1S018.
Full textMultiple sclerosis (MS) affects around 80. 000 people in France. Magnetic resonance imaging (MRI) is an essential tool for diagnosis of MS and MRI-derived surrogate markers such as MS lesion volumes are often used as measures in MS clinical trials for the development of new treatments. The manual segmentation of these MS lesions is a time-consuming task that shows high inter- and intra-rater variability. We developed an automatic workflow for the segmentation of focal MS lesions on MRI. The segmentation method is based on the robust estimation of a parametric model of the intensities of the brain; lesions are detected as outliers to the model. We proposed two methods to include spatial information in the segmentation using mean shift and graph cut. We performed a quantitative evaluation of our workflow using synthetic and clinical images of two different centers to verify its accuracy and robustness
ASLANI, SHAHAB. "Deep learning approaches for segmentation of multiple sclerosis lesions on brain MRI." Doctoral thesis, Università degli studi di Genova, 2020. http://hdl.handle.net/11567/997626.
Full textPeruch, Francesco. "(SEMI)-AUTOMATED ANALYSIS OF MELANOCYTIC LESIONS." Doctoral thesis, Università degli studi di Padova, 2015. http://hdl.handle.net/11577/3424252.
Full textIl Melanoma è una forma molto aggressiva di cancro alla pelle la cui incidenza è costantemente aumentata negli ultimi 50 anni. Una diagnosi precoce unita ad una rapida asportazione risulta indispensabile per migliorare il tasso di sopravvivenza e richiede una analisi periodica ed accurata della lesioni melanocitiche del paziente. Abbiamo sviluppato una soluzione hardware e software chiamata Mole Mapper per assistere i deramtologi durante l'intero processo di diagnosi. L'obiettivo è permettere un incremento dell'accuratezza della diagnosi velocizzando al contempo l'intero processo. Tali caratteristiche si sono ottenute grazie ad un'analisi automatica delle immagini dermatoscopiche che individua ed evidenza al dermatologo le informazioni più significative. In questa tesi presentiamo 3 principali algoritmi che sono stati implementati in Mole Mapper: Una robusta segmentazione di lesioni melanocitiche, che risulta il punto di partenza di ogni altro algoritmo di elaborazioni di immagini e permette l'estrazione di informazioni utili riguardanti la forma e la dimensione delle lesioni. Tale algoritmo supera in accuratezza e velocità lo stato dell'arte attuale, con una precisione paragonabile ad un dermatologo esperto ed un tempo di esecuzione compatibile con l'elaborazione video realtime; Un algoritmo di depilazione digitale, che garantisce miglior precisione e robustezza agli altri algoritmi di elaborazione di immagini a fornisce al dermatologo un immagine priva di peli da impiegare nel processo di valutazione. La nostra proposta supera l'accuratezza dello stato dell'arte richiedendo solo una frazione del tempo di esecuzione, tanto da poter essere integrata su dispositivi mobili all'interno di una GUI interattiva. Un algoritmo di registrazione, per studiare l'evoluzione delle lesioni nel tempo evidenziando ogni possibile anomalia o variazione. Data la mancanza di un approccio standard al problema, abbiamo caratteriizzato gli obbiettivi ed i vincoli a cui sottostare proponendo quindi un approccio con un tempo di esecuzione compatibile con le necessità del Mole Mapper ed un accuratezza paragonabile a quella di un operatore umano.
Cui, Shenshen. "Fully Automatic Segmentation of White Matter Lesions from Multispectral Magnetic Resonance Imaging Data." Thesis, Uppsala University, Department of Information Technology, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-122650.
Full textA fully automatic white matter lesion segmentation method has been developed and evaluated. The method uses multispectral magnetic resonance imaging (MRI) data (T1,T2 and Proton Density). First fuzzy c means (FCM) was used to segment normal brain tissues (white matter,grey matter, and cerebrospinal fluid). The holes in normal white matter were used to sample the WML intensities in the different images. The segmentation of WML was optimized by a graph cut approach. The method was trained by using 9 manually segmented datasets and evaluated by comparison to 11 other manually segmented, and visually evaluated, datasets. The graph cut part of the automatic segmentation requires, on average, 30 seconds per dataset. The results correlated well (r=0.954) to a manually created reference that was supervised by two neuro radiologists.
Key Words: White matter lesion, automatic segmentation, graph cuts, MRI, PIVUS
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/.
Full textMokhomo, 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.
Full textThe 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.
Veronese, Elisa. "Methods for segmentation and characterization of multiple sclerosis cortical lesions from MRI data." Doctoral thesis, Università degli studi di Padova, 2012. http://hdl.handle.net/11577/3422439.
Full textQuesta tesi tratta l’analisi automatica di immagini di risonanza magnetica cerebrale in soggetti affetti da sclerosi multipla. In particolare, l’analisi è volta sia a una stima quantitativa del carico di lesioni corticali presenti a causa del decorso della malattia, sia a una caratterizzazione del tipo di lesioni presenti basata sul loro grado di infiammazione. La sclerosi multipla è una malattia infiammatoria a decorso cronico che colpisce il sistema nervoso centrale, provocandone una progressiva distruzione della mielina in più aree. Per frequenza, nel giovane adulto è la seconda malattia neurologica e la prima di tipo infiammatorio cronico. Inoltre, la sclerosi multipla può essere considerata anche come malattia sociale, con un’elevata ricaduta economica, sia diretta che indiretta: la diminuzione o la perdita dell’autonomia porta alla progressiva impossibilità di svolgere una qualsiasi attività lavorativa fino all’incapacità di condurre una vita indipendente. A questo si aggiungano il costo delle cure e dell’assistenza necessarie. Benché le cause siano ancora in parte sconosciute, molto è stato fatto nel chiarire le diverse fasi del processo infiammatorio che caratterizza tale patologia, permettendo così di arrivare a una diagnosi e a un trattamento precoce che consentono di ridurre gli effetti della malattia. Le lesioni causate dalla sclerosi multipla risultano visibili grazie a particolari tecniche di acquisizione di immagini basate sulla risonanza magnetica. In particolare negli ultimi decenni si sono studiate e messe a punto diverse sequenze di risonanza ottimizzate per la visualizzazione delle lesioni in materia bianca. Il quadro delle tecniche a disposizione qualora si vogliano studiare lesioni in materia grigia risulta invece meno completo, soprattutto a causa del fatto che la scoperta di un coinvolgimento della materia grigia nella malattia è molto più recente. La verifica dell’evoluzione e della comparsa di nuove lesioni è importante dal momento che consente di monitorare il progredire di una malattia caratterizzata da fasi acute intervallate a periodi di quiescenza più o meno lunghi. Per questo motivo i soggetti affetti da sclerosi multipla vengono periodicamente sottoposti a esami di risonanza magnetica. Ogni successiva valutazione da parte del medico neurologo dipenderà da quanto evidenziato dalle immagini acquisite. In questo senso è fondamentale che il medico sia ben allenato nella valutazione di immagini di risonanza, e che ponga particolare attenzione non solo nell’individuare la comparsa di nuove lesioni, ma anche nel riconoscere la presenza di lesioni già presenti in esami precedenti, che possono essere progredite nella forma, nelle dimensioni e nel grado di attività. La lettura di un esame di risonanza magnetica richiede tempo e attenzione, ed è inevitabilmente soggetta all’errore umano che caratterizza qualsiasi valutazione manuale. Per questo, benché sia impensabile prescindere dalla valutazione del medico, una tecnica di analisi automatica di immagini di risonanza magnetica cerebrale che sia in grado di evidenziare la presenza di lesioni da sclerosi multipla può rappresentare un valido aiuto alla refertazione, sia in termini di tempo che di accuratezza. In questa tesi si descriveranno le tecniche di risonanza magnetica a disposizione per una miglior visualizzazione delle lesioni corticali. Su queste si procederà alla segmentazione del tessuto di interesse, ossia del volume di materia grigia. In seguito verrà descritta la tecnica proposta per il riconoscimento delle regioni patologiche corticali. Infine sarà descritto un primo tentativo di caratterizzazione delle diverse lesioni corticali, basato sulla valutazione del grado di attività di ciascuna lesione.
Militzer, Arne [Verfasser], and Joachim [Akademischer Betreuer] Hornegger. "Boosting Methods for Automatic Segmentation of Focal Liver Lesions / Arne Militzer. Gutachter: Joachim Hornegger." Erlangen : Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), 2015. http://d-nb.info/1075480299/34.
Full textBakas, Spyridon. "Computer-aided localisation, segmentation and quantification of focal liver lesions in contrast-enhanced ultrasound." Thesis, Kingston University, 2014. http://eprints.kingston.ac.uk/30592/.
Full textLindemalm, Karlsson Josefin. "Deep Learning-Based Automated Segmentation and Detection of Chondral Lesions on the Distal Femur." Thesis, KTH, Fysik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-253077.
Full textSkador i knäledens brosk kan diagnostiseras i ett tidigt stadie med hjälp av MR. Segmentering och visualisering av skadorna, samt ledens struktur i helhet, bidrar till en förbättrad kommunikation mellan radiolog och remitterande läkare. Det kan också underlätta för att ställa diagnos eller utföra operationsplanering. I dagsläget finns flertalet studier som påvisar goda resultat för segmentering av större strukturer, t.ex. ben och brosk. Det finns dock få studier som studerar segmentering av skador i ledbrosk. Genom att automatisera segmenteringsprocessen kan både tid och pengar sparas. Detta eftersom att manuell segmentering är mycket tidskrävande. I detta arbete kommer ett U-Net baserat convolutional neural network att användas för att utföra automatisk segmentering av skador på distala femur i knäleden. Nätverket kommer att tränas med två olika tekniker, batch normalization och dropout. Nätverket kommer att tränas med data som är hämtad från Episurf Medicals databas och består av MR sekvenser. Nätverket kommer att tränas och utvärderas med hjälp av två metoder, en segmenteringsmetod och detekteringsmetod. Den högsta uppnådda dice koefficienten och sensitiviteten vid utvärderingen av segmenteringsmetoden uppmätte 0,4059 ± 0,1833 och 0,4591 ± 0,2387. Den upnåddes med hjälp av batch normalization och 260 MR sekvenser för träning och testning. För detektionsmetoden kunde programmet identifiera 81,8% av skadorna synliga på MR sekvenserna. Även om tekniken och datan som används behöver optimeras, så visar det uppnådda resultatet på bra förutsättningar för fortsatta studier och i framtiden möjligen även implementering av tekniken.
Geremia, Ezequiel. "Spatial random forests for brain lesions segmentation in MRIs and model-based tumor cell extrapolation." Phd thesis, Université Nice Sophia Antipolis, 2013. http://tel.archives-ouvertes.fr/tel-00838795.
Full textBAIL, LAURENT. "Segmentation automatique en i. R. M. Des lesions de sclerose en plaque : correlation avec l'atrophie du corps calleux." Lille 2, 1994. http://www.theses.fr/1994LIL2M251.
Full textBernart, Eliezer Emanuel. "Detecção e qualificação de lesões melanocíticas através de evidências locais e de contexto." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2016. http://hdl.handle.net/10183/134372.
Full textIn this work, a novel unsupervised method for melanocytic macroscopic image segmentation is proposed considering suspicious regions, and also a novel approach for lesion classification using local and context evidence to estimate a probabilistic index of malignity or benignity in each lesion. The proposed method segment the macroscopic images in three types of disjoint regions: ‘healthy skin’, ‘suspicious region’ and ‘lesion’. Suspicious areas are refined using stochastic texture features also in an unsupervised approach, resulting in a binary mask discriminating skin and lesion. The resulting masks present an XOR error similar to other state-of-art methods. In the next step, the image is segmented using a superpixels algorithm and subregions that intersect the obtained mask categorized as local evidence. A specialized representation describes color and texture information present in the local evidence region. The border of the segmented skin lesion defines the context evidence and using a supervised approach, local and context evidence are combined and classified independently. With the evidence classification results is possible to obtain a probabilistic index of malignity and benignity associated to each lesion, and considering a tolerance value is possible to identify potential malignant lesions. The results achieved with the proposed method are promissing and present greater accuracy than other techniques in the literature, even with a greater XOR error in segmentation step, confirming the proposed method’s potential to discriminate benignant and malignant melanocytic lesions.
Engström, Messén Matilda, and Elvira Moser. "Pre-planning of Individualized Ankle Implants Based on Computed Tomography - Automated Segmentation and Optimization of Acquisition Parameters." Thesis, KTH, Fysik, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-297674.
Full textFotledens komplexa anatomi ger upphov till en ideal balans mellan rörlighetoch stabilitet, vilket i sin tur möjliggör gång. Fotledens anatomi förändras när en skada uppstår, vilket kan påverka rörligheten och stabiliteten samt orsaka intensiv smärta. En skada i talusbenets ledbrosk eller i det subkondrala benet på talusdomen benämns som en Osteochondral Lesion of the Talus(OLT). En metod att behandla OLTs är att ersätta den del brosk eller bensom är skadat med ett implantat. Episurf Medical utvecklar och producerar individanpassade implantat (Episealers) och tillhörande nödvändiga kirurgiska instrument genom att, bland annat, skapa en motsvarande 3D-modell av fotleden (talus-, tibia- och fibula-benen) baserat på en skanning med antingen magnetisk resonanstomografi (MRI) eller datortomografi (CT). I dagsläget kan de 3D-modeller som baseras på MRI-skanningar skapas automatiskt, medan de 3D-modeller som baseras på CT-skanningar måste skapas manuellt - det senare ofta tidskrävande. I detta examensarbete har ett U-net-baserat Convolutional Neuralt Nätverk (CNN) tränats för att automatiskt kunna segmentera 3D-modeller av fotleder baserat på CT-bilder. Vidare har de speciferade parametrarna i Episurfs CT-protokoll för fotleden som skickas ut till klinikerna utvärderats, detta för att optimera bildkvaliteten på de CT-bilder som används för implantatspositionering och design. Det tränade nätverkets prestanda utvärderades med hjälp av Dicekoefficienten (DC) med en fem-delad korsvalidering. Nätverket åstadkom engenomsnittlig DC på 0.978±0.009 för talusbenet, 0.779±0.174 för tibiabenet, och 0.938±0.091 för fibulabenet. Värdena för talus och fibula var adekvata och jämförbara med resultaten presenterade i tidigare forskning. På grund av bakgrundsartefakter i bilderna blev den DC som nätverket åstadkom för sin segmentering av tibiabenet lägre än tidigiare forskningsresultat. För att korrigera för bakgrundsartefakterna kommer ett brusreduceringsfilter implementeras
Flores, Eliezer Soares. "Segmentação de lesões melanocíticas usando uma abordagem baseada no aprendizado de dicionários." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2015. http://hdl.handle.net/10183/115219.
Full textSegmentation is an essential step for the automated pre-screening of melanocytic lesions. In this work, a new method for segmenting melanocytic lesions in standard camera images (i.e., macroscopic images) is presented. Initially, to reduce unwanted artifacts, shading effects are attenuated in the macroscopic image and a pre-segmentation is obtained using a scheme that combines the wavelet transform and the watershed transform. Afterwards, a textural variation image designed to enhance the skin lesion against the background is obtained, and the presegmented skin lesion region is used to learn an initial dictionary and an initial representation via a nonnegative matrix factorization method. An unsupervised and non-parametric version of the information-theoretic dictionary learning method is proposed to optimize this representation by selecting the subset of atoms that maximizes the learned dictionary compactness and representation. Finally, the skin lesion image is represented using the learned dictionary and segmented with the normalized graph cuts method. Our experimental results based on a widely used image dataset suggest that the proposed method tends to provide more accurate skin lesion segmentations than comparable state-of-the-art methods (in terms of the XOR error).
Roura, Pérez Eloy. "Automated methods on magnetic resonance brain imaging in multiple sclerosis." Doctoral thesis, Universitat de Girona, 2016. http://hdl.handle.net/10803/394030.
Full textEn aquesta tesi ens centrem, per una part, en el pre-processat de la imatge per tal d'eliminar el soroll i corregir les inhomogeneïtats en les intensitats, ambdós errors introduïts per l'escàner. A més hem contribuït també amb una nova tècnica basada en un algoritme de “región growing” per tal de segmentar el cervell de dins de tota la imatge del cap. Incloem com a pre-processat el registre d'imatges, on hem proposat una “pipeline" mitjançant la informació de múltiples modalitats per tal de millorar els resultats d'aquest procés. Per altra banda, hem estudiat també les tècniques actuals de detecció i segmentació de lesions en la matèria blanca, proposant un mètode nou basat en anteriors propostes. Així doncs, presentem una eina automàtica capaç de detectar i segmentar lesions en la matèria blanca de pacients d'Esclerosi Múltiple i Lupus.
Demel, Jan. "Segmentace 3D obrazových dat s využitím grafové reprezentace." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2014. http://www.nusl.cz/ntk/nusl-220863.
Full textMartin, Matthieu. "Reconstruction 3D de données échographiques du cerveau du prématuré et segmentation des ventricules cérébraux et thalami par apprentissage supervisé." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI118.
Full textAbout 15 million children are born prematurely each year worldwide. These patients are likely to suffer from brain abnormalities that can cause neurodevelopmental disorders: cerebral palsy, deafness, blindness, intellectual development delay, … Studies have shown that the volume of brain structures is a good indicator which enables to reduce and predict these risks in order to guide patients through appropriate care pathways during childhood. This thesis aims to show that 3D ultrasound could be an alternative to MRI that would enable to quantify the volume of brain structures in all premature infants. This work focuses more particularly on the segmentation of the lateral ventricles (VL) and thalami. Its four main contributions are: the development of an algorithm which enables to create 3D ultrasound data from 2D transfontanellar ultrasound of the premature brain, the segmentation of thigh quality he lateral ventricles and thalami in clinical time and the learning by a convolutional neural networks (CNN) of the anatomical position of the lateral ventricles. In addition, we have created several annotated databases in partnership with the CH of Avignon. Our reconstruction algorithm was used to reconstruct 25 high-quality ultrasound volumes. It was validated in-vivo where an accuracy 0.69 ± 0.14 mm was obtained on the corpus callosum. The best segmentation results were obtained with the V-net, a 3D CNN, which segmented the CVS and the thalami with respective Dice of 0.828± 0.044 and 0.891±0.016 in a few seconds. Learning the anatomical position of the CVS was achieved by integrating a CPPN (Compositional Pattern Producing Network) into the CNNs. It significantly improved the accuracy of CNNs when they had few layers. For example, in the case of the 7-layer V-net network, the Dice has increased from 0.524± 0.076 to 0.724±0.107. This thesis shows that it is possible to automatically segment brain structures of the premature infant into 3D ultrasound data with precision and in a clinical time. This proves that high quality 3D ultrasound could be used in clinical routine to quantify the volume of brain structures and paves the way for studies to evaluate its benefit to patients
Eltayef, Khalid Ahmad A. "Segmentation and lesion detection in dermoscopic images." Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/16211.
Full textPons, Rodríguez Gerard. "Computer-aided lesion detection and segmentation on breast ultrasound." Doctoral thesis, Universitat de Girona, 2014. http://hdl.handle.net/10803/129453.
Full textAquesta 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
Maier, Oskar [Verfasser]. "Decision forest variants for brain lesion segmentation / Oskar Maier." Lübeck : Zentrale Hochschulbibliothek Lübeck, 2017. http://d-nb.info/1137396520/34.
Full textFreire, Paulo Guilherme de Lima. "Segmentação de placas de esclerose múltipla em imagens de ressonância magnética usando modelos de mistura de distribuições t-Student e detecção de outliers." Universidade Federal de São Carlos, 2016. https://repositorio.ufscar.br/handle/ufscar/7736.
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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
Multiple Sclerosis (MS) is an inflammatory demyelinating (that is, with myelin loss) disease of the Central Nervous System (CNS). It is considered an autoimmune disease in which the immune system wrongly recognizes the myelin sheath of the CNS as an external element and attacks it, resulting in inflammation and scarring (sclerosis) of multiple areas of CNS’s white matter. Multi-contrast magnetic resonance imaging (MRI) has been successfully used in diagnosing and monitoring MS due to its excellent properties such as high resolution and good differentiation between soft tissues. Nowadays, the preferred method to segment MS lesions is the manual segmentation, which is done by specialists with limited help of a computer. However, this approach is tiresome, expensive and prone to error due to inter- and intra-variability between observers caused by low contrast on lesion edges. The challenge in automatic detection and segmentation of MS lesions in MR images is related to the variability of size and location of lesions, low contrast due to partial volume effect and the high range of forms that lesions can take depending on the stage of the disease. Recently, many researchers have turned their efforts into developing techniques that aim to accurately measure volumes of brain tissues and MS lesions, and also to reduce the amount of time spent on image analysis. In this context, this project proposes the study and development of an automatic computational technique based on an outlier detection approach, Student’s t-distribution finite mixture models and probabilistic atlases to segment and measure MS lesions volumes in MR images.
Esclerose Múltipla (EM) é uma doença inflamatória e desmielinizante (isto é, com perda de mielina) do sistema nervoso central (SNC). É considerada uma doença autoimune a qual o sistema imunológico reconhece erroneamente a bainha de mielina do SNC como um elemento externo e então a ataca, resultando em inflamação e formação de cicatrizes gliais (escleroses) em múltiplas áreas da substância branca do SNC. O imageamento multi- contraste por ressonância magnética (RM) tem sido usado clinicamente com muito sucesso para o diagnóstico e monitoramento da EM devido às suas excelentes propriedades como alta resolução e boa diferenciação de tecidos moles. Atualmente, o método utilizado para a segmentação de lesões de EM é o delineamento manual em imagens 3D de RM, o qual é realizado por especialistas com ajuda limitada do computador. Entretanto, tal procedimento é custoso e propenso à variabilidade inter e intraobservadores devido ao baixo contraste das bordas das lesões. A grande dificuldade na detecção e segmentação automáticas das le- sões de EM em imagens de RM está associada às suas variações no tamanho e localização, baixo contraste decorrente do efeito de volume parcial e o amplo espectro de aparências (realçadas, não-realçadas, black-holes) que elas podem ter, dependendo do estado evolutivo da doença. Atualmente, vários pesquisadores têm voltado seus esforços para o desenvol- vimento de técnicas que visam diminuir o tempo gasto na análise das imagens e medir, de maneira mais precisa, o volume dos tecidos cerebrais e das lesões de EM. Nesse contexto, este projeto propõe o estudo e o desenvolvimento de uma técnica computacional automá- tica, baseada na abordagem de detecção de outliers e usando modelos de misturas finitas de distribuições t-Student e atlas probabilísticos para a segmentação e medição do volume de lesões de EM em imagens de RM.
FAPESP: 2014/00019-6
Li, Xiang. "Depth data improves non-melanoma skin lesion segmentation and diagnosis." Thesis, University of Edinburgh, 2012. http://hdl.handle.net/1842/5867.
Full textKobold, Jonathan. "Deep Learning for lesion and thrombus segmentation from cerebral MRI." Thesis, Université Paris-Saclay (ComUE), 2019. http://www.theses.fr/2019SACLE044.
Full textDeep learning, the world's best set of methods for identifying ob-jects on images. Stroke, a deadly disease whose treatment requiresidentifying objects on medical imaging. Sounds like an obvious com-bination yet it is not trivial to marry the two. Segmenting the lesionfrom stroke MRI has had some attention in literature but thrombussegmentation is still uncharted area. This work shows that contem-porary convolutional neural network architectures cannot reliablyidentify the thrombus on stroke MRI. Also it is demonstrated whythese models don't work on this problem. With this knowledge arecurrent neural network architecture, the logic LSTM, is developedthat takes into account the way medical doctors identify the throm-bus. Not only this architecture provides the first reliable thrombusidentification, it also provides new insights to neural network design.Especially the methods for increasing the receptive field are enrichedwith a new parameter free option. And last but not least the logicLSTM also improves the results of lesion segmentation by providinga lesion segmentation with human level performance
Kaur, Ravneet. "THRESHOLDING METHODS FOR LESION SEGMENTATION OF BASAL CELL CARCINOMA IN DERMOSCOPY IMAGES." OpenSIUC, 2017. https://opensiuc.lib.siu.edu/dissertations/1367.
Full textDong, 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.
Full textMaster 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.
Thieu, Quang Tung. "Segmentation by convex active contour models : application to skin lesion and medical images." Paris 13, 2013. http://www.theses.fr/2013PA132063.
Full textGonzalez, Ana Guadalupe Salazar. "Structure analysis and lesion detection from retinal fundus images." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/6456.
Full textRaina, Kevin. "Machine Learning Methods for Brain Lesion Delineation." Thesis, Université d'Ottawa / University of Ottawa, 2020. http://hdl.handle.net/10393/41156.
Full textGong, Hao. "Segmentation d'images couleurs et multispectrales de la peau." Phd thesis, Université de Grenoble, 2013. http://tel.archives-ouvertes.fr/tel-00934789.
Full textBrosch, Tom. "Efficient deep learning of 3D structural brain MRIs for manifold learning and lesion segmentation with application to multiple sclerosis." Thesis, University of British Columbia, 2016. http://hdl.handle.net/2429/58427.
Full textApplied Science, Faculty of
Electrical and Computer Engineering, Department of
Graduate
Pescia, Daniel. "Segmentation des tumeurs du foie sur des images CT." Phd thesis, Ecole Centrale Paris, 2011. http://tel.archives-ouvertes.fr/tel-00649030.
Full textMoltz, Jan Hendrik [Verfasser], Horst Karl [Akademischer Betreuer] Hahn, Andreas [Akademischer Betreuer] Nüchter, and Ginneken Bram [Akademischer Betreuer] van. "Lesion segmentation and tracking for CT-based chemotherapy monitoring / Jan Hendrik Moltz. Betreuer: Horst Karl Hahn. Gutachter: Horst Karl Hahn ; Andreas Nüchter ; Bram van Ginneken." Bremen : IRC-Library, Information Resource Center der Jacobs University Bremen, 2013. http://d-nb.info/1087285054/34.
Full textDaviller, Clément. "Quantification de la perfusion myocardique en imagerie de perfusion par résonance magnétique : modèles et classification non-supervisée." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE1208/document.
Full textCardiovascular diseases and in particular coronary heart disease are the main cause of death worldwide with 17.9 million deaths in 2012. Cardiac MRI is a particularly interesting tool for understanding and evaluating heart disease, including ischemic heart disease. Its diagnostic contribution is often major and it provides information that is not accessible by other imaging modalities. The work carried out during this thesis focuses more specifically on the so-called myocardium perfusion test, which consists in studying the distribution of a contrast agent within the heart muscle during its first passage. In clinical practice, this examination is often limited to the clinician's visual analysis, allowing him to identify the culprit artery and deduce the impacted territory. However, this technique is relative and does not quantify myocardial blood flow. In recent years, an increasing number of techniques have emerged to enable this quantification at all stages of processing, from acquisition to the measurement itself. We first established a treatment pipeline to combine these approaches and evaluate them using a digital phantom and clinical data. We demonstrated that the Bayesian approach is able to quantify myocardium perfusion and its superiority in evaluating the arrival time of the indicator bolus compared to the Fermi model. In addition, the Bayesian approach provides additional interesting information such as the probability density function of the measurement and the uncertainty of the residual function, which makes it possible to know the reliability of the measurement carried out, in particular by observing the distribution of the probability density function of the measurement. Finally, we proposed an algorithm for segmentation of myocardial lesions, using the spatial and temporal dimensions of infusion data. This technique allows an objective and precise segmentation of the hypoperfused region allowing a measurement of myocardial blood flow over an area of tissue which behavior is homogeneous and which average signal measurement allows an increase in the contrast-to-noise ratio. In the cohort of 30 patients, the variability of myocardial blood flow measurements performed on voxels detected by this technique was significantly lower than that of measurements performed on voxels in manually defined areas (mean difference=0.14, 95% CI[0.07, 0.2]) and those performed on voxels in areas defined using the bullseye method (mean difference=0.25, 95% CI[0.17, 0.36])
Moussallem, Mazen. "Optimisation de la délimitation automatique des tumeurs pulmonaires à partir de l'imagerie TEP/TDM pour les planifications dosimétriques des traitements par radiothérapie." Phd thesis, Université Claude Bernard - Lyon I, 2011. http://tel.archives-ouvertes.fr/tel-00864905.
Full textChmelík, 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.
Full textGlaister, Jeffrey Luc. "Automatic segmentation of skin lesions from dermatological photographs." Thesis, 2013. http://hdl.handle.net/10012/7718.
Full textHsu, Yu-ting, and 徐于婷. "Image Segmentation of Lesions in Brain Magnetic Resonance Imaging." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/41786073121372140182.
Full text國立雲林科技大學
工業工程與管理研究所碩士班
101
MRI can provide information on pathological changes in brain tissue to the doctors for diagnosis and treatment. With the advancement of technology, clinical practice has integrated computer-aided measurement and diagnosis, and the cutting algorithm can be used to cut out the required focal area accurately, in order to effectively diagnose the focal area. This study used Active Contours using Level Sets (ACLS) to cut out the boundary of tumor and edema. Before the segmentation, the image preprocessing technique N3 was used for image non-homogeneity removal, and the influence of non-homogeneity removal on segmentation performance was discussed. The segmentation effect of ACLS algorithm was influenced by the parameter combinations in the algorithm. In this study, Grid Search was used to search for the optimum parameter combination, so as to discuss the influence of parameter optimization of ACLS on the segmentation performance. Therefore, there were four experimental combinations in this study: (1) without non-homogeneity removal without parameter optimization, (2) without non-homogeneity removal with parameter optimization, (3) with non-homogeneity removal without parameter optimization and (4) with non-homogeneity removal with parameter optimization. The four combinations were processed by ACLS algorithm, and then the Jaccard Similarity was used to measure the segmentation performance of the four combinations. The Analysis of Variance (ANOVA) was used to calculate whether various factors resulted in significance difference in image segmentation, and then the paired-samples t test was conducted to test the significance difference among various combinations. The result of ANOVA showed that in the tumor segmentation performance, the non-homogeneity removal results in significance difference in segmentation performance. There was no significance difference in the segmentation performance without parameter optimization, and the interaction between two factors did not reached significance difference. In the edema segmentation performance, there was no significance difference whether or not there were non-homogeneity removal and parameter optimization, and the interaction between two factors had not reached significance difference. The result of paired-sample t test found that for the segmentation of tumor area, the Jaccard Similarity average value of non-homogeneity removal and parameter optimization in test data set was 0.7899. The segmentation performance was the best and was significantly better than the other combinations. For the segmentation of edema, the Jaccard Similarity average value of non-homogeneity removal and parameter optimization in test data set was 0.7604. The segmentation performance was also the best and was significantly better than the other combinations.
Vieira, Pedro Miguel. "Hierarchical classification of lesions in wireless capsule endoscopy exams." Doctoral thesis, 2021. http://hdl.handle.net/1822/75508.
Full textA cápsula endoscópica é um dispositivo médico que tem como como principal vantagem a possibilidade de visualizar todo o trato gastrointestinal. Este exame não invasivo é especialmente usado e vantajoso para o diagnóstico de patologias do intestino delgado, já que a endoscopia convencional é um exame invasivo que não possibilita a visualização deste órgão. Para analisar os exames de cápsula endoscópica o pessoal médico necessita de treino especializado, tendo sido provado que a quantidade massiva de imagens de cada exame pode levar à existência de erros médicos e uma propensão a que exista um subdiagnóstico de algumas patologias. Esta tese teve como objetivo o desenvolvimento de sistemas de deteção automática de diferentes tipos de lesões presentes no intestino delgado. Estes métodos envolveram o uso de algoritmos de segmentação baseados em métodos probabilísticos (nomeadamente o Expectation- Maximization), com a apresentação de um método de aceleração da convergência do algoritmo e do desenvolvimento de um novo método para melhorar as fronteiras de segmentação, baseado em Campos Aleatórios de Markov. Além disso, foram estudadas diferentes metodologias de classificação supervisionada, desde classificadores mais simples e classificadores ensemble para deteção de lesões individuais, e redes neuronais convolucionais e segmentação de instâncias para deteção e segmentação de multi-patologias. Com o apoio do Hospital de Braga, foi efetuado um estudo clínico com o método desenvolvido para deteção automática de angioectasias. Este trabalho teve como principal objetivo comparar a eficiência e performance deste método com a performance de diferentes médicos a analisar exames de cápsula endoscópica. Os diferentes métodos desenvolvidos demonstraram resultados superiores aos encontrados na bibliografia mais recente. É importante referir que o trabalho desenvolvido nesta tese permitiu uma melhor análise à necessidade de uma maior implantação de métodos de deteção de lesões em sistemas de cápsula endoscópica, tal como a necessidade de maiores e melhores estudos clínicos, tal como a disponibilização de melhores bases de dados públicas.
The wireless capsule endoscopy is a medical device with the main advantage of being able to visualize the whole gastrointestinal tract. This non-invasive exam is specially used for the diagnosis of small bowel pathologies, since the conventional endoscopy is not able to visualize this organ. To analyze these exams the medical staff need specialized training and it was recently proven that the massive quantity of images that are generated lead to medical errors and consequently the sub diagnosis of certain pathologies. In this thesis the main objective was to develop systems for automatic detection of different lesions present in the small bowel. These developments included the use of segmentation algorithms based on probabilistic methods (namely the Expectation-Maximization), with the presentation of an acceleration method and a new approach for improving the borders of the segmentation based on Markov Random Fields. Beyond that, several supervised classification strategies were studied, with the use of single-based classifiers and ensemble-based classifiers for detection of single lesions and convolutional neural networks, and instance segmentation for multipathology detection and segmentation. With the support of Hospital of Braga, a clinical studied was performed with the developed method for angioectasia detection. This work had the main purpose of comparing the efficiency and performance of the method with the performance of different physicians when analyzing wireless capsule endoscopy exams. The developed methods were tested in different applications and it was found that the performance was improved when compared to the most recent bibliography. It is important to state that all this work allowed to conclude that these systems need to have a greater implantation in the clinical practice. While there is a lot of advances in computer vision methods for lesion detection, there are still lacking better clinical studies and better and bigger public databases to improve the testing of the methodologies.
The author was funded by the grant SFRH/BD/92143/2013 from the Portuguese Foundation for Science and Technology (FCT), with funds from the European Social Fund (FSE), under the Human Capital Operational Programme (POCH) from Portugal 2020 Programme.
Gentile, Giordano. "Facing the current challenges in multiple sclerosis lesions: from automated segmentation to assessing the role of inflammation in neurodegeneration." Doctoral thesis, 2022. http://hdl.handle.net/2158/1265556.
Full textZhang, Shu-Wei, and 張書瑋. "Level Set Method with Cell Structure and Graph Partition Prior for Segmentation of Sonographic Breast Lesions." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/69445629325251191059.
Full text國立臺灣大學
醫學工程學研究所
95
Automatic boundary extraction of multiple targets of interest in an ultrasound image can not only help clinicians find out most perceptible objects, but also save sonologists’ time for boundary delineation. Moreover, it is potentially helpful for the novice instruction and medical research. In this paper we propose a new method combining the level set method with the cell competition structure information and graph partition prior in ultrasound image segmentation. Because of the intrinsic properties in ultrasound images, such as low contrast, high noises, speckle, artifacts, tissue related textures and so on, it is difficult to identify the blurred boundaries or weak edges in ultrasound images. Since the cell competition algorithm can divide the ROI (Region of Interest) into several prominent components, which can be parts of the desired target, tissue structures, artifacts, and so on, we first apply it to capture the most likely visually perceived boundaries in ultrasound images. Then we regard the boundaries of the prominent components as shape prior knowledge and integrate it into the level set energy function. Next, we adjust the importance of the boundary information by a spatially variant band constructed from the second smallest eigenvector via the constrained normalized-Cut. The proposed method unifies the region-based, and boundary-based information as well as the shape prior in the level set energy function. The active contours are attracted by the visually perceived boundaries defined by the cell structures and the broken edges and weak edges of the cell structure are overcome by incorporating the second smallest eigenvector computed by the constrained normalized cut. The proposed algorithm has been validated on 472 breast sonograms which comprise 221 malignant and 251 benign cases. The result shows the proposed method can detect weak edges successfully in ultrasound images. Moreover, the boundaries derived by the proposed method are comparable to the manually delineated boundaries and robust in reproducibility.
Weng, Ching-Jung, and 翁靖容. "Segmentation of white matter lesions in multispectral MR image using Independent Component Analysis and Support Vector Machine." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/w86qf4.
Full text國立陽明大學
生物醫學影像暨放射科學系
104
Magnetic resonance images (MRI) are used to quantify brain volume because of its excellent soft-tissue contrast and high spatial resolution. Volume changes in whole brain, gray/white matter and lesion can provide information for diagnosis or prognosis of various brain diseases. Currently, most software for brain MRI analysis only use T1 weighted image (T1WI). But when there are white matter lesions in Multiple sclerosis (MS) patients, it can’t be properly distinguished because of the similar signal intensity between lesion and white matter on TIWI. Compared to using single weighted image, multispectral MRI contain different image contrast which is useful for segmentation. The purpose of this study is to use Independent component analysis (ICA) and Support vector machine (SVM) to identify white matter lesion correctly using multispectral MRI. At first, synthetic MS images were analyzed including: registration, noise correction, uniformity correction and skull stripping. Secondly, SVM and ICA+SVM methods were used to analyze three groups of multispectral MR image. Thirdly, using sensitivity, positive predictive value (PPV) and Tanimoto index (TI) to evaluate the accuracy. Lastly, clinical MR images were analyzed. For synthetic MS images, the result of lesion classification by applying ICA+SVM method to analyze T1WI, T2WI and FLAIR images had the highest PPV. The sensitivity, PPV and TI for this technique were over 0.75/0.7/0.6. As for clinical MS data experiments, ICA+SVM method can identify more lesion than freesurfer which only use T1WI in the analysis.
Jodas, Danilo Samuel. "Segmentation and classification of structures of the carotid and coronary arteries for image-based evaluation of atherosclerotic lesions." Doctoral thesis, 2017. https://hdl.handle.net/10216/108560.
Full textJodas, Danilo Samuel. "Segmentation and classification of structures of the carotid and coronary arteries for image-based evaluation of atherosclerotic lesions." Tese, 2017. https://hdl.handle.net/10216/108560.
Full text