Teses / dissertações sobre o tema "Segmentation des images échographiques"
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Dhibi, Mounir. "Segmentation et quantification volumique des thromboses veineuses : application aux images échographiques". Télécom Bretagne, 2006. http://www.theses.fr/2006TELB0014.
Texto completo da fonteDahdouh, Sonia. "Filtrage, segmentation et suivi d'images échographiques : applications cliniques". Phd thesis, Université Paris Sud - Paris XI, 2011. http://tel.archives-ouvertes.fr/tel-00647326.
Texto completo da fonteTauber, Clovis. "Filtrage anisotrope robuste et segmentation par B-spline snake : application aux images échographiques". Phd thesis, Toulouse, INPT, 2005. http://oatao.univ-toulouse.fr/7357/1/tauber1.pdf.
Texto completo da fontePaulhac, Ludovic. "Outils et méthodes d'analyse d'images 3D texturées : application à la segmentation des images échographiques". Phd thesis, Université François Rabelais - Tours, 2009. http://tel.archives-ouvertes.fr/tel-00576507.
Texto completo da fonteIonescu, Gelu. "Segmentation et recalage d'images échographiques par utilisation de connaissances physiologiques et morphologiques". Phd thesis, Université Joseph Fourier (Grenoble), 1998. http://tel.archives-ouvertes.fr/tel-00005189.
Texto completo da fonteGarnier, Carole. "Segmentation de la prostate pour la thérapie par Ultrasons Haute Intensité guidée par l'image". Phd thesis, Université Rennes 1, 2009. http://tel.archives-ouvertes.fr/tel-00498035.
Texto completo da fonteSandoval, Niño Zulma. "Planning and guidance of ultrasound guided High Intensity Focused Ultrasound cardiac arrhythmia therapy". Thesis, Rennes 1, 2015. http://www.theses.fr/2015REN1S044/document.
Texto completo da fonteThe work presented in this document aims at developing new image-processing methods to improve the planning and guidance of transesophageal HIFU atrial fibrillation therapy. This document is divided into two parts, namely therapy planning and therapy guidance. We first propose novel therapy planning methods that exploit high-resolution pre-operative CT or MRI information to extract patient-specific anatomical details and to define future therapeutic procedures. Our specific methodological contributions concern the following: an automatically-refined atlas-based segmentation approach to extract the left atrium and pulmonary veins; the delineation of the lesion lines on the original or segmented volume; and the reconstruction of a volume adapted to future intraoperative transesophageal navigation. Secondly, our proposal of a novel registration approach for use in therapy guidance aligns intraoperative 2D ultrasound with preoperative 3D CT information. This approach first carries out a systematic statistical evaluation to select the best similarity measure for our application and then takes advantage of the geometrical constraints of the transesophageal HIFU probe to simplify the registration process. Our proposed methods have been evaluated on digital and/or physical phantoms and on real clinical data
Leclerc, Sarah Marie-Solveig. "Automatisation de la segmentation sémantique de structures cardiaques en imagerie ultrasonore par apprentissage supervisé". Thesis, Lyon, 2019. http://www.theses.fr/2019LYSEI121.
Texto completo da fonteThe analysis of medical images plays a critical role in cardiology. Ultrasound imaging, as a real-time, low cost and bed side applicable modality, is nowadays the most commonly used image modality to monitor patient status and perform clinical cardiac diagnosis. However, the semantic segmentation (i.e the accurate delineation and identification) of heart structures is a difficult task due to the low quality of ultrasound images, characterized in particular by the lack of clear boundaries. To compensate for missing information, the best performing methods before this thesis relied on the integration of prior information on cardiac shape or motion, which in turns reduced the adaptability of the corresponding methods. Furthermore, such approaches require man- ual identifications of key points to be adapted to a given image, which makes the full process difficult to reproduce. In this thesis, we propose several original fully-automatic algorithms for the semantic segmentation of echocardiographic images based on supervised learning ap- proaches, where the resolution of the problem is automatically set up using data previously analyzed by trained cardiologists. From the design of a dedicated dataset and evaluation platform, we prove in this project the clinical applicability of fully-automatic supervised learning methods, in particular deep learning methods, as well as the possibility to improve the robustness by incorporating in the full process the prior automatic detection of regions of interest
Assadzadeh, Djafar. "Traitement des images échographiques". Paris 13, 1986. http://www.theses.fr/1986PA132013.
Texto completo da fonteMeghoufel, Ali. "Analyse des images échographiques du tendon équin". Mémoire, École de technologie supérieure, 2011. http://espace.etsmtl.ca/889/1/MEGHOUFEL_Ali.pdf.
Texto completo da fonteHadjerci, Oussama. "Détection automatique du nerf dans les images échographiques". Thesis, Orléans, 2017. http://www.theses.fr/2017ORLE2006/document.
Texto completo da fonteRegional anesthesia presents an interesting alternative or complementary act to general anesthesia in many surgical procedures. It reduces pain scores, improves postoperative mobility and facilitates earlier hospital discharge. Ultrasound-Guided Regional Anesthesia (UGRA) has been gaining importance in the last few years, offering numerous advantages over alternative methods of nerve localization (neurostimulation or paraesthesia). However, nerve detection is one of the most difficult tasks that anesthetists can encounter in the UGRA procedure. The context of the present work is to provide practitioners with a method to facilitate and secure the practice of UGRA. However, automatic detection and segmentation in ultrasound images is still a challenging problem in many medical applications. This work addresses two main issues. The first one, we propose an algorithm for nerve detection and segmentation in ultrasound images, this method is composed of a pre-processing, texture analysis and machine learning steps. In this part of work, we explore two new approaches ; one to characterize the nerve and the second for selecting the minimum redundant and maximum relevant features. The second one, we studied the nerve detection in consecutive ultrasound frames. We have demonstrated that the development of an algorithm based on the temporal coherence of the position, the shape and the confidence measure of the classification, allows to generate a robust segmentation. In this work, we also propose a new model of shape based on a set of intervals landmarks able to adapt to the nerve shape under a morphological variations
Ploquin, Marie. "Super résolution pour l'amélioration de la résolution des images échographiques". Thesis, Tours, 2011. http://www.theses.fr/2011TOUR4025/document.
Texto completo da fonteMedical Imaging Ultrasound has several advantages such as its safety, ease of use, the diversity of organs that can be imaged and the low cost of this imaging mode. However, the images obtained by ultrasound suffer from relatively low resolution compared to others than can be obtain with an MRI or using X-rays. The major challenge of medical ultrasound is the ability to produce images with a resolution much finer, without modifying the nominal frequency.Work has been undertaken in this direction for some time. Several approaches have been explored. Most of the work done so far has been to work on the ultrasound acquiring device and particularly on ultrasonic probes, with main objective to increase the frequency of ultrasound used. This approach has led to the existence of high-resolution ultrasound, but with the reduction of the depth of exploration as an important limitation.Another approach is to treat numerically conventional ultrasound images to improve resolution. This method has several advantages, it allows to circumvent such difficulties caused by the reduction of depth of field due to the increase in the ultrasonic frequency.In this thesis, we present a method to improve the resolution of ultrasound images. The thesis to be to adapt this method to ultrasound imaging and to provide an estimate of the maximum theoretical resolution achieved by this method based on image parameters including SNR and the bandwidth of the PSF. We also proposed a method of superresolution suitable for ultrasound. By providing on improving theoretical superresolution and adaptation to the particular case of ultrasound, this thesis opens up on improving the resolution of ultrasound images by processing the signal and the image
Li, Zhongqiang. "Segmentation of textured images". Thesis, University of Central Lancashire, 1991. http://clok.uclan.ac.uk/20270/.
Texto completo da fonteTorre, Alcoceba Margarita. "Model-Based Segmentation of Images". Doctoral thesis, Universitat Autònoma de Barcelona, 2020. http://hdl.handle.net/10803/670932.
Texto completo da fonteLa fotografía congela en un instante los datos que luego se pueden extraer, interpretar y transformar con el tiempo para comunicar información en diferentes formatos. Hacer mapas a partir de fotografías fue una revolución en la cartografía. Los avances en la visión por computador están ayudando a lograr la próxima revolución en esta disciplina, que apunta a una información geográfica cada vez más detallada que se requiere en períodos de tiempo más cortos. De esta manera, el proceso que va de la imagen a un mapa se ha vuelto cada vez más automático. Las imágenes ya capturadas con cámaras digitales de alta resolución se colocan automáticamente en la posición correcta del terreno como si fuera una hoja que lo cubre, gracias a los modelos digitales del terreno, obteniendo así ortofotomapas. En estas circunstancias, la única carga que queda por aligerar es la extracción de los elementos topográficos, sin perder la precisión y la calidad de la interpretación que hasta ahora ha sido proporcionada por operadores humanos. Esta investigación se centra en el desarrollo de nuevos métodos por ordenador que facilitan estas tareas de extracción de información de imágenes aéreas. Comenzamos con el desarrollo de una estrategia para extraer parcelas semi-automáticamente de las imágenes. Este enfoque utiliza la respuesta casi homogénea de las parcelas y cómo esta respuesta difiere de la obtenida de sus vecinas. El proceso se lleva a cabo mediante el método en el que las regiones adyacentes compiten para poseer un píxel. Cuando las líneas de contraste de las imágenes también se tienen en cuenta, es posible ampliar la metodología anterior para extraer carreteras. En ambos casos es necesario guiar todo el proceso, no sólo por los puntos dados por un operador, sino por el modelo del elemento a extraer. El modelo ayuda a refinar los resultados obtenidos. Cuando Deep Learning irrumpió en la escena de Visión por Computador, todos los procesos de clasificación de imágenes mejoraron. Proponemos una aventura conjunta entre una red profunda y un método de minimización de energía guiado por un modelo que mejore los beneficios de cada componente. Este enfoque reduce al mínimo la necesidad de interacción humana y obtiene buenos resultados.
Photography freezes in an instant the data that can later be extracted, interpreted and transformed over time to communicate information in different formats. Making maps from photographs was a revolution in cartography. Advances in Computer Vision are helping to bring about the next revolution in this discipline, which aims at more and more detailed geographic information which is required in shorter periods of time. In this way, the process that goes from image to a map has become increasingly automatic. The images already captured with high-resolution digital cameras are automatically placed in the correct position of the terrain as if they were a sheet that covers it, thanks to the digital terrain models, thus obtaining orthophotomaps. In these circumstances, the only burden that remains to be lightened is the extraction of the topographic elements, without losing the precision and quality of interpretation that until now has been provided by human operators. This research focuses on the development of new computerized methods that facilitate these tasks of extracting information from aerial images. We start with the development of a strategy to semi-automatically extract fields from the images. This approach uses the almost homogeneous response of the fields and how this response differs from that obtained from their neighbors. The process is carried out by means of the method in which adjacent regions compete to own a pixel. When the contrast lines of the images are also taken into account, it is possible to extend the previous methodology to extract roads. In both cases it is necessary to guide the entire process, not only by the points given by an operator, but by the model of the element to be extracted. The model helps to refine the results obtained. When Deep Learning burst onto the Computer Vision scene, all the processes of image classification were upended. So, we propose a joint venture between a deep network and an energy-minimization model-guided radiometric method that improves the benefits of each component. This approach reduces to a minimum the need for human interaction and obtains reliable results.
Demirkol, Onur Ali. "Segmentation Of Torso Ct Images". Master's thesis, METU, 2006. http://etd.lib.metu.edu.tr/upload/2/12607431/index.pdf.
Texto completo da fontewatershed transformation and region merging. Moreover, a comparative analysis is performed among these methods to obtain the most efficient segmentation method for each tissue and organ in torso. Some improvements are proposed for increasing accuracy of some image segmentation methods.
Muller, Simon Adriaan. "Planar segmentation of range images". Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/80168.
Texto completo da fonteENGLISH ABSTRACT: Range images are images that store at each pixel the distance between the sensor and a particular point in the observed scene, instead of the colour information. They provide a convenient storage format for 3-D point cloud information captured from a single point of view. Range image segmentation is the process of grouping the pixels of a range image into regions of points that belong to the same surface. Segmentations are useful for many applications that require higherlevel information, and with range images they also represent a significant step towards complete scene reconstruction. This study considers the segmentation of range images into planar surfaces. It discusses the theory and also implements and evaluates some current approaches found in the literature. The study then develops a new approach based on the theory of graph cut optimization which has been successfully applied to various other image processing tasks but, according to a search of the literature, has otherwise not been used to attempt segmenting range images. This new approach is notable for its strong guarantees in optimizing a specific energy function which has a rigorous theoretical underpinning for handling noise in images. It proves to be very robust to noise and also different values of the few parameters that need to be trained. Results are evaluated in a quantitative manner using a standard evaluation framework and datasets that allow us to compare against various other approaches found in the literature. We find that our approach delivers results that are competitive when compared to the current state-of-the-art, and can easily be applied to images captured with different techniques that present varying noise and processing challenges.
AFRIKAANSE OPSOMMING: Dieptebeelde is beelde wat vir elke piksel die afstand tussen die sensor en ’n spesifieke punt in die waargenome toneel, in plaas van die kleur, stoor. Dit verskaf ’n gerieflike stoorformaat vir 3-D puntwolke wat vanaf ’n enkele sigpunt opgeneem is. Die segmentasie van dieptebeelde is die proses waarby die piksels van ’n dieptebeeld in gebiede opgedeel word, sodat punte saam gegroepeer word as hulle op dieselfde oppervlak lê. Segmentasie is nuttig vir verskeie toepassings wat hoërvlak inligting benodig en, in die geval van dieptebeelde, verteenwoordig dit ’n beduidende stap in die rigting van volledige toneel-rekonstruksie. Hierdie studie ondersoek segmentasie waar dieptebeelde opgedeel word in plat vlakke. Dit bespreek die teorie, en implementeer en evalueer ook sekere van die huidige tegnieke wat in die literatuur gevind kan word. Die studie ontwikkel dan ’n nuwe tegniek wat gebaseer is op die teorie van grafieksnit-optimering wat al suksesvol toegepas is op verskeie ander beeldverwerkingsprobleme maar, sover ’n studie op die literatuur wys, nog nie gebruik is om dieptebeelde te segmenteer nie. Hierdie nuwe benadering is merkbaar vir sy sterk waarborge vir die optimering van ’n spesifieke energie-funksie wat ’n sterk teoretiese fondasie het vir die hantering van geraas in beelde. Die tegniek bewys om fors te wees tot geraas sowel as die keuse van waardes vir die min parameters wat afgerig moet word. Resultate word geëvalueer op ’n kwantitatiewe wyse deur die gebruik van ’n standaard evalueringsraamwerk en datastelle wat ons toelaat om hierdie tegniek te vergelyk met ander tegnieke in die literatuur. Ons vind dat ons tegniek resultate lewer wat mededingend is ten opsigte van die huidige stand-van-die-kuns en dat ons dit maklik kan toepas op beelde wat deur verskeie tegnieke opgeneem is, alhoewel hulle verskillende geraastipes en verwerkingsuitdagings bied.
Yan, Jinnan. "Camouflaged Object Segmentation in Images". University of Dayton / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1576064709283297.
Texto completo da fonteOliver, i. Malagelada Arnau. "Automatic mass segmentation in mammographic images". Doctoral thesis, Universitat de Girona, 2007. http://hdl.handle.net/10803/7739.
Texto completo da fonteThis thesis deals with the detection of masses in mammographic images. As a first step, Regions of Interests (ROIs) are detected in the image using templates containing a probabilistic contour shape obtained from training over an annotated set of masses. Firstly, PCA is performed over the training set, and subsequently the template is formed as an average of the gradient of eigenmasses weighted by the top eigenvalues. The template can be deformed according to each eigenmass coefficient. The matching is formulated in a Bayesian framework, where the prior penalizes the deformation, and the likelihood requires template boundaries to agree with image edges. In the second stage, the detected ROIs are classified into being false positives or true positives using 2DPCA, where the new training set now contains ROIs with masses and ROIs with normal tissue. Mass density is incorporated into the whole process by initially classifying the two training sets according to breast density. Methods for breast density estimation are also analyzed and proposed. The results are obtained using different databases and both FROC and ROC analysis demonstrate a better performance of the approach relative to competing methods.
Rieck, Christian Marshall. "Segmentation of Medical Images Using CBR". Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2007. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-8821.
Texto completo da fonteThis paper describes a case based reasoning system that is used to guide the parameters of a segmentation algorithm. Instead of using a fixed set of parameters that gives the best average result over all images, the parameteres are tuned to maximize the score for each image separately. The system's foundation is a set of 20 cases that each contains one 3D MRI image and the parameters needed for its optimal segmentation. When a new image is presented to the system a new case is generated and compared to the other cases based on image similarity. The parameters from the best matching case are then used to segment the new image. The key issue is the use of an iterative approach that lets the system adapt the parameters to suit the new image better, if necessary. Each iteration contains a segmentation and a revision of the result, and this is done until the system approves the result. The revision is based on metadata stored in each case to see if the result has the expected properties as defined by the case. The results show that combining case based reasoning and segmentation can be applied within image processing. This is valid for choosing a good set of starting parameters, and also for using case specific knowledge to guide their adaption. A set of challenges for future research is identified and discussed at length.
Ree, Eirik. "Segmentation of Kidneys from MR-Images". Thesis, Norwegian University of Science and Technology, Department of Computer and Information Science, 2005. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-9212.
Texto completo da fonteDet har blitt utviklet en metode for semi-automatisk segmentering av nyrer fra 2D og 3D MR-bilder. Algoritmen foregår som en kombinasjon av en watershed segmentering og en modellbasert segmentering. For å løse problemet med at aktive konturer krever en svært god initialisering, brukes resultatet av watershed segmenteringen til å lage initielle konturer. Resultatet har blitt en god og fleksibel algoritme som gir gode resultater og lett kan brukes også på andre segmenteringsoppgaver.
Fernández, Francisco Cruz. "Nuclei Segmentation on Bright-Field Images". Thesis, Uppsala University, Department of Information Technology, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-129475.
Texto completo da fonteNuclei segmentation is a common and complicated task in image analysis. There is no general solution for the problem, and depending on the image characteristics the segmentation can be performed in different ways. Bright-field images add some complications to the problem; the color of some elements of the image is close to the color of the nuclei, making the segmentation difficult. In this thesis some methods are presented to complete this task, two classifiers, minimum distance classifier and multilayer perceptron are tested to enhance the nuclei. After the classification, threshold methods together with morphological operations are used to get the segmentation of the nuclei with an accuracy around 85%.
Gasslander, Maja. "Segmentation of Clouds in Satellite Images". Thesis, Linköpings universitet, Datorseende, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-128802.
Texto completo da fonteJu, Chen. "Edge-enhanced segmentation for SAR images". Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ34190.pdf.
Texto completo da fonteMatalas, Ioannis. "Segmentation techniques suitable for medical images". Thesis, Imperial College London, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.339149.
Texto completo da fonteSu, Qi, e 蘇琦. "Segmentation and reconstruction of medical images". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2008. http://hub.hku.hk/bib/B41897067.
Texto completo da fonteMarais, Patrick Craig. "The segmentation of sparse MR images". Thesis, University of Oxford, 1998. http://ora.ox.ac.uk/objects/uuid:ac0e8f6c-51b7-42e0-8079-4d9a83b578b2.
Texto completo da fonteZeng, Ziming. "Medical image segmentation on multimodality images". Thesis, Aberystwyth University, 2013. http://hdl.handle.net/2160/17cd13c2-067c-451b-8217-70947f89164e.
Texto completo da fonteAkinyemi, Akinola Olanrewaju. "Atlas-based segmentation of medical images". Thesis, University of Glasgow, 2011. http://theses.gla.ac.uk/2623/.
Texto completo da fonteWang, Yang. "Segmentation Guided Registration for Medical Images". Ohio University / OhioLINK, 2005. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1126905907.
Texto completo da fonteSu, Qi. "Segmentation and reconstruction of medical images". Click to view the E-thesis via HKUTO, 2008. http://sunzi.lib.hku.hk/hkuto/record/B41897067.
Texto completo da fonteBasarab, Adrian. "Estimation du mouvement dans des séquences d'images échographiques". Phd thesis, Lyon, INSA, 2008. http://theses.insa-lyon.fr/publication/2008ISAL0051/these.pdf.
Texto completo da fonteThis PhD work deals with motion estimation in ultrasound image sequences and its application to static elastography of the thyroid. The aim of elastography is to characterize the elasticity of biological soft tissues. For this, motion tracking is processed between images acquired while a compressive force is applied to the examined medium. Ln order to discriminate the healthy and diseased tissues, a high level of motion estimation accuracy is required. To achieve this accuracy we propose a 2-D motion estimation method applied to radiofrequency images issued from a specifie beamforming technique. Our method uses four phase images obtained with multidimensional analytic signais. This original approach allowed us to propose an analytic solution to the local motion estimation problem. Our method is shown to be more efficient than classical techniques in terms of accuracy, applicability of low sampled images and computation lime. Moreover, we propose the use of a bilinear displacement model in order to take into account the complexity of tissue movements under freehand compression. A spatio-temporal approach allows us to extend our method to motion estimation with ultrasound image sequence We also propose a novel parameter more appropriate for visualizing thyroid tumors
Massich, i. Vall Joan. "Deformable object segmentation in ultra-sound images". Doctoral thesis, Universitat de Girona, 2013. http://hdl.handle.net/10803/128329.
Texto completo da fonteEn aquest treball, es proposa un sistema automàtic per generar delineacions acurades de lesions de mama en imatges d’ultrasò. El sistema proposat planteja el problema de trobar la delineació corresponent a la minimització d’un sistema probabilístic multiclasse mitjançant el tall de mínim cost del graf que representa la imatge. El sistema representa la imatge com un conjunt de regions i infereix una classe per cada una d’aquestes regions a partir d’uns models estadístics obtinguts d’unes imatges d’entrenament. El principal avantatge del sistema és que divideix la tasca en subtasques més fàcils d’adreçar i després soluciona el problema de forma global
Caesar, Jenny. "Segmentation of the Brain from MR Images". Thesis, Linköping University, Department of Biomedical Engineering, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-3568.
Texto completo da fonteKTH, Division of Neuronic Engineering, have a finite element model of the head. However, this model does not contain detailed modeling of the brain. This thesis project consists of finding a method to extract brain tissues from T1-weighted MR images of the head. The method should be automatic to be suitable for patient individual modeling.
A summary of the most common segmentation methods is presented and one of the methods is implemented. The implemented method is based on the assumption that the probability density function (pdf) of an MR image can be described by parametric models. The intensity distribution of each tissue class is modeled as a Gaussian distribution. Thus, the total pdf is a sum of Gaussians. However, the voxel values are also influenced by intensity inhomogeneities, which affect the pdf. The implemented method is based on the expectation-maximization algorithm and it corrects for intensity inhomogeneities. The result from the algorithm is a classification of the voxels. The brain is extracted from the classified voxels using morphological operations.
Pescia, Daniel. "Segmentation of liver tumors on CT images". Thesis, Châtenay-Malabry, Ecole centrale de Paris, 2011. http://www.theses.fr/2011ECAP0002/document.
Texto completo da fonteThis thesis is dedicated to 3D segmentation of liver tumors in CT images. This is a task of great clinical interest since it allows physicians benefiting from reproducible and reliable methods for segmenting such lesions. Accurate segmentation would indeed help them during the evaluation of the lesions, the choice of treatment and treatment planning. Such a complex segmentation task should cope with three main scientific challenges: (i) the highly variable shape of the structures being sought, (ii) their similarity of appearance compared with their surrounding medium and finally (iii) the low signal to noise ratio being observed in these images. This problem is addressed in a clinical context through a two step approach, consisting of the segmentation of the entire liver envelope, before segmenting the tumors which are present within the envelope. We begin by proposing an atlas-based approach for computing pathological liver envelopes. Initially images are pre-processed to compute the envelopes that wrap around binary masks in an attempt to obtain liver envelopes from estimated segmentations of healthy liver parenchyma. A new statistical atlas is then introduced and used to segmentation through its diffeomorphic registration to the new image. This segmentation is achieved through the combination of image matching costs as well as spatial and appearance priors using a multiscale approach with MRF. The second step of our approach is dedicated to lesions segmentation contained within the envelopes using a combination of machine learning techniques and graphbased methods. First, an appropriate feature space is considered that involves texture descriptors being determined through filtering using various scales and orientations. Then, state of the art machine learning techniques are used to determine the most relevant features, as well as the hyperplane that separates the feature space of tumoral voxels to the ones corresponding to healthy tissues. Segmentation is then achieved by minimizing an MRF energy that combines class probabilities and neighbor constraints. Promising results demonstrate the potentials of our method
Madden, Matthew J. "Segmentation of images with low-contrast edges". Morgantown, W. Va. : [West Virginia University Libraries], 2007. https://eidr.wvu.edu/etd/documentdata.eTD?documentid=5299.
Texto completo da fonteTitle from document title page. Document formatted into pages; contains xi, 104 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 99-104).
Cai, Hongmin. "Quality enhancement and segmentation for biomedical images". Click to view the E-thesis via HKUTO, 2007. http://sunzi.lib.hku.hk/hkuto/record/B39380130.
Texto completo da fonteEzzadeen, Hani. "Extraction and segmentation of MRI brain images". Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=97949.
Texto completo da fonteIn this thesis, we explain the research we have implemented to extract the brain from T1-weighted MRI images, and then segment the brain into the three prominent compartments (i.e. the cerebellum and the two hemispheres of the cerebrum). The brain extraction is implemented using morphological operations after thresholding. The brain segmentation, however, is implemented in two separate steps. The first step segments the two hemispheres by approximating the midsagittal surface using mainly Radon transform. The second step segments the cerebellum using an atlas-based contour as an initial contour for the gradient vector flow active contour algorithm.
Validation tests have been performed for the brain extraction and cerebellum segmentation methods.
Chen, Xiaohua. "Simultaneous segmentation and registration of medical images". Thesis, University of Oxford, 2005. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426410.
Texto completo da fonteWang, Li. "Segmentation of branching structures from medical images". Thesis, University of Warwick, 2004. http://wrap.warwick.ac.uk/61391/.
Texto completo da fonteCai, Hongmin, e 蔡宏民. "Quality enhancement and segmentation for biomedical images". Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2007. http://hub.hku.hk/bib/B39380130.
Texto completo da fonteNoriega, Leonardo Antonio. "The colorimetric segmentation of textured digital images". Thesis, Southampton Solent University, 1998. http://ssudl.solent.ac.uk/2444/.
Texto completo da fonteEltayef, Khalid Ahmad A. "Segmentation and lesion detection in dermoscopic images". Thesis, Brunel University, 2017. http://bura.brunel.ac.uk/handle/2438/16211.
Texto completo da fonteSégonne, Florent 1976. "Segmentation of medical images under topological constraints". Thesis, Massachusetts Institute of Technology, 2005. http://hdl.handle.net/1721.1/36136.
Texto completo da fonteIncludes bibliographical references (p. 135-142).
Major advances in the field of medical imaging over the past two decades have provided physicians with powerful, non-invasive techniques to probe the structure, function, and pathology of the human body. This increasingly vast and detailed amount of information constitutes a great challenge for the medical imaging community, and requires significant innovations in all aspect of image processing. To achieve accurate and topologically-correct delineations of anatomical structures from medical images is a critical step for many clinical and research applications. In this thesis, we extend the theoretical tools applicable to the segmentation of images under topological control, apply these new concepts to broaden the class of segmentation methodologies, and develop generally applicable and well-founded algorithms to achieve accurate segmentations of medical images under topological constraints. First, we introduce a digital concept that offers more flexibility in controlling the topology of digital segmentations. Second, we design a level set framework that offers a subtle control over the topology of the level set components. Our method constitutes a trade-off between traditional level sets and topology-preserving level sets.
(cont.) Third, we develop an algorithm for the retrospective topology correction of 3D digital segmentations. Our method is nested in the theory of Bayesian parameter estimation, and integrates statistical information into the topology correction process. In addition, no assumption is made on the topology of the initial input images. Finally, we propose a genetic algorithm to accurately correct the spherical topology of cortical surfaces. Unlike existing approaches, our method is able to generate several potential topological corrections and to select the maximum-a-posteriori retessellation in a Bayesian framework. Our approach integrates statistical, geometrical, and shape information into the correction process, providing optimal solutions relatively to the MRI intensity profile and the expected curvature.
by Florent Ségonne.
Ph.D.
Schneider, Michael K. (Michael Klaus). "Multiscale methods for the segmentation of images". Thesis, Massachusetts Institute of Technology, 1996. http://hdl.handle.net/1721.1/11027.
Texto completo da fonteIncludes bibliographical references (p. 95-97).
by Michael K. Schneider.
M.S.
Jones, Jonathan-Lee. "2D and 3D segmentation of medical images". Thesis, Swansea University, 2015. https://cronfa.swan.ac.uk/Record/cronfa42504.
Texto completo da fonteKozinski, Mateusz. "Segmentation of facade images with shape priors". Thesis, Paris Est, 2015. http://www.theses.fr/2015PESC1017/document.
Texto completo da fonteThe aim of this work is to propose a framework for facade segmentation with user-defined shape priors. In such a framework, the user specifies a shape prior using a rigorously defined shape prior formalism. The prior expresses a number of hard constraints and soft preference on spatial configuration of segments, constituting the final segmentation. Existing approaches to the problem are affected by a compromise between the type of constraints, the satisfaction of which can be guaranteed by the segmentation algorithm, and the capability to approximate optimal segmentations consistent with a prior. In this thesis we explore a number of approaches to facade parsing that combine prior formalism featuring high expressive power, guarantees of conformance of the resulting segmentations to the prior, and effective inference. We evaluate the proposed algorithms on a number of datasets. Since one of our focus points is the accuracy gain resulting from more effective inference algorithms, we perform a fair comparison to existing methods, using the same data term. Our contributions include a combination of graph grammars for expressing variation of facade structure with graphical models encoding the energy of models of given structures for different positions of facade elements. We also present the first linear formulation of facade parsing with shape priors. Finally, we propose a shape prior formalism that enables formulating the problem of optimal segmentation as the inference in a Markov random field over the standard four-connected grid of pixels. The last method advances the state of the art by combining the flexibility of a user-defined grammar with segmentation accuracy that was reserved for frameworks with pre-defined priors before. It also enables handling occlusions by simultaneously recovering the structure of the occluded facade and segmenting the occluding objects. We believe that it can be extended in many directions, including semantizing three-dimensional point clouds and parsing images of general urban scenes
Martin, 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.
Texto completo da fonteAbout 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
Quartararo, John David. "Semi-automated segmentation of 3D medical ultrasound images". Worcester, Mass. : Worcester Polytechnic Institute, 2008. http://www.wpi.edu/Pubs/ETD/Available/etd-020509-161314/.
Texto completo da fonteKeywords: 3d ultrasound; ultrasound; image processing; image segmentation; 3d image segmentation; medical imaging Includes bibliographical references (p.142-148).
Wu, Di. "Segmentation, registration,and selective watermarking of retinal images". Texas A&M University, 2005. http://hdl.handle.net/1969.1/3739.
Texto completo da fonteStanier, Jeffrey. "Segmentation and editing of 3-dimensional medical images". Thesis, University of Ottawa (Canada), 1994. http://hdl.handle.net/10393/10031.
Texto completo da fonte