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Дисертації з теми "Traitement d'images – Médecine"
Aurengo, André. "Analyse factorielle des séquences d'images en médecine nucléaire." Paris 11, 1989. http://www.theses.fr/1989PA112413.
Повний текст джерелаMagimel-Pelonnier, Vincent. "Traitement d'images : vers l'extraction automatique de paramètres : application à la cardiologie en médecine nucléaire." Bordeaux 1, 1985. http://www.theses.fr/1985BOR10528.
Повний текст джерелаSaint-Jean, Patrick. "Processus de production et d'expérimentation biologiques et médicales par l'analyse de texture prétopologique et la robotique de laboratoire." Paris 13, 1989. http://www.theses.fr/1989PA132023.
Повний текст джерелаMichoud, Edouard. "Analyse d'images dynamiques en biologie et médecine : applications à la microcirculation clinique et expérimentale." Université Joseph Fourier (Grenoble), 1990. http://www.theses.fr/1990GRE19002.
Повний текст джерелаCoste, Eric. "Reconstruction d'une arborescence spatiale à partir d'un nombre minimal de projections : application à l'angiographie numérisée." Lille 1, 1996. https://pepite-depot.univ-lille.fr/LIBRE/Th_Num/1996/50376-1996-204.pdf.
Повний текст джерелаProvent, Pierre. "Segmentation d'images par analyse statistique de textures : application aux images échocardiographiques." Paris 12, 1991. http://www.theses.fr/1991PA120049.
Повний текст джерелаPiedbois, Pascal. "Stratégies d'informatisation d'un service de cancérologie, et nouvelles approches du traitement graphique de l'information." Paris 12, 1994. http://www.theses.fr/1994PA120023.
Повний текст джерелаHannequin, Pascal. "Applications des méthodes statistiques d'analyse multivariée au traitement des séries d'images en médecine nucléaire et en microscopie électronique." Reims, 1989. http://www.theses.fr/1989REIMS006.
Повний текст джерелаDebon, Renaud. "Analyse d'images échographiques de loesophage, reconstruction 3D et interprétation." Rennes 1, 2005. http://www.theses.fr/2005REN1S196.
Повний текст джерелаThis work concerns the approach evaluations, which are oriented « knowledge based system » for medical images interpretation applied to esophagus echoendoscopy and the development of aid system for tumor staging. It's shown how the intelligent approaches (expert system and information fusion) can allow rationalizing the using of a priori knowledge. The pertinent extraction of anatomic structures, in our case, esophagus structure, becomes a natural application in the knowledge engineering. This extraction is based on the image segmentation. The required robustness for these algorithms impose the advanced architectures development allowing the compensation of low numerical content of these images. Three concrete examples are detailed : 2D extraction of the esophagus' interface, 3D extraction of the esophagus' interfaces and spatial following with 3D reconstruction of the aorta. Knowledge is represented by static or dynamical model (fuzzy, geometric or evidential models). The approach using the complementarities of probabilities and fuzzy logic to obtain presentation « exact » of knowledge a priori. Fuzzy models and statistic reality are synchronized by a knowledge base. It's shown that all components can be integrated in a coherent architecture hierarchically organized
Gautier, Laurent. "Aide à la ségmentation d'images par la théorie des croyances : application aux séquences d'images IRM du rachis lombaire." Littoral, 2001. http://www.theses.fr/2001DUNK0057.
Повний текст джерелаThe current need for the fusion of data in image processing results directly from the multiplication of the data available starting from or of the systems of medical imagery which are used jointly to observe a same phenomenon under different aspects. The problem, which we pose in fusion data, can be expressed like a problem of decision on the truth or the probability of a proposal being given one or more information resulting from a same sensor or sensors different. With regard to the applications, it is a question of taking into account the vague, incomplete and dubious aspect of the data learned on each sensor and the redundant, complementary and conflict aspect of the whole of information. The complex characteristics of the informative systems must be introduced into all the stages of a process of fusion, from the assumptions the decision. The general goal of the thesis is a contribution to the segmentation 2D of images by belief theory applied to the sequences of images by Magnetic Resonance Imagery (MRI) of the lumbar rachis. The medical objective, in the long term, is the total and local analysis curve of the spinal column for the study of its deformations 3D starting from sequences obtained by MRI. Within the framework of this work, we were interested at the first stage : the segmentation of the vertebrae. The use of the traditional methods of segmentation did not enable us to obtain the contour of the vertebrae on images MRI acquisited. We then decided to exploit the contribution of the methods of data fusion to help us in the validation of the points resulting from the segmentation by active contour. For that, we propose a generic diagram of fusion data within the framework of our application. It diagram must allow of exploit the data exit of different level of analyze (at level of pixel, at level of contour) for extract the information the more reliable and the more exact in a goal of assistance with segmentation, in order to take in account the effect of partial volume dependent with protocol of acquisition MRI. For the architecture of fusion suggested, based on the theory of beliefs, we tried to justify the choice : a priori knowledge ; frame of discernement ; model of representation ; parameters essential to the discrimination of the starting assumptions ; strategy of fusion, distributed or global ; decision criteria. We discuss the validity of the found results, of the prospects considered and we finish for example of computation result of deformation 3D spinal column and vertebra
Книги з теми "Traitement d'images – Médecine"
Riviere, Patrick La, and Mark A. Anastasio. Emerging Imaging Technologies in Medicine. Taylor & Francis Group, 2020.
Знайти повний текст джерелаEmerging Imaging Technologies in Medicine. Taylor & Francis Group, 2012.
Знайти повний текст джерелаRiviere, Patrick La, and Mark A. Anastasio. Emerging Imaging Technologies in Medicine. Taylor & Francis Group, 2012.
Знайти повний текст джерелаRiviere, Patrick La, and Mark A. Anastasio. Emerging Imaging Technologies in Medicine. Taylor & Francis Group, 2012.
Знайти повний текст джерелаTavares, Joao, and R. M. Natal Jorge. Computational Vision and Medical Image Processing V: Proceedings of the 5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing. Taylor & Francis Group, 2015.
Знайти повний текст джерелаTavares, Joao, and R. M. Natal Jorge. Computational Vision and Medical Image Processing V: Proceedings of the 5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing. Taylor & Francis Group, 2015.
Знайти повний текст джерелаComputational Vision and Medical Image Processing V: Proceedings of the 5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing. Taylor & Francis Group, 2015.
Знайти повний текст джерелаTavares, Joao, and R. M. Natal Jorge. Computational Vision and Medical Image Processing V: Proceedings of the 5th Eccomas Thematic Conference on Computational Vision and Medical Image Processing. Taylor & Francis Group, 2015.
Знайти повний текст джерелаComputational Vision and Medical Image Processing IV: Vipimage 2013. Taylor & Francis Group, 2013.
Знайти повний текст джерелаTavares, Joao Manuel Rs, and Jorge R. M. Natal. Computational Vision and Medical Image Processing IV: Vipimage 2013. Taylor & Francis Group, 2013.
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