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Extraction de caractéristiques et apprentissage statistique pour l'imagerie biomédicale cellulaire et tissulaire

Alexis Zubiolo 1
1 MORPHEME - Morphologie et Images
CRISAM - Inria Sophia Antipolis - Méditerranée , IBV - Institut de Biologie Valrose : U1091, Laboratoire I3S - SIS - Signal, Images et Systèmes
Abstract : The purpose of this Ph.D. thesis is to study the classification based on morphological features of cells and tissues taken from biomedical images. The goal is to help medical doctors and biologists better understand some biological phenomena. This work is spread in three main parts corresponding to the three typical problems in biomedical imaging tackled. The first part consists in analyzing endomicroscopic videos of the colon in which the pathological class of the polyps has to be determined. This task is performed using a supervised multiclass machine learning algorithm combining support vector machines and graph theory tools. The second part concerns the study of the morphology of mice neurons taken from fluorescent confocal microscopy. In order to obtain a rich information, the neurons are imaged at two different magnifications, the higher magnification where the soma appears in details, and the lower showing the whole cortex, including the apical dendrites. On these images, morphological features are automatically extracted with the intention of performing a classification. The last part is about the multi-scale processing of digital histology images in the context of kidney cancer. The vascular network is extracted and modeled by a graph to establish a link between the architecture of the tumor and its pathological class.
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Submitted on : Thursday, July 6, 2017 - 10:48:31 AM
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Alexis Zubiolo. Extraction de caractéristiques et apprentissage statistique pour l'imagerie biomédicale cellulaire et tissulaire. Autre. Université Nice Sophia Antipolis, 2015. Français. ⟨NNT : 2015NICE4117⟩. ⟨tel-01290131⟩



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