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<dc:title xml:lang="fr">Imagerie spectro-polarimétrique : système, algorithmes et biopsie optique</dc:title>
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<dc:subject xml:lang="fr">Polarimétrie</dc:subject>
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<dc:subject xml:lang="fr">Imagerie de polarisation</dc:subject>
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<dcterms:abstract xml:lang="fr">Le cancer est une pathologie que l’on se doit de détecter le plus tôt possible si l’on veut accroître les chances de guérison. Ces travaux étudient l’apport de la signature polarimétrique à la caractérisation et l’identification des tissus cancéreux. Il s’agit d’extraire des images multidimensionnelles de polarisation des informations physiques qui caractérisent les constituants de l’objet bien au-delà de l’information visuelle des images d’intensité. Durant cette thèse, un imageur de Mueller, POLARIS, a pu être mis en place ainsi que les outils de traitement et de calibration adaptés. Une méthode de segmentation d’images de Mueller en condition d’éclairement non homogène a été proposée. Une première base d’images multi-spectrales polarimétriques de tissus sains et pathogènes chez la souris a été constituée. Une approche originale a enfin été proposée en se basant sur les forêts aléatoires pour extraire parmi un ensemble de paramètres physiques un jeu de paramètres permettant de différencier les zones saines des zones pathogènes aux différentes longueurs d’ondes de travail. Une comparaison est proposée avec la littérature et permet de valider l’approche.</dcterms:abstract>
<dcterms:abstract xml:lang="en">Cancer is a pathology that must be detected as soon as possible in order to increase the chances of recovery. These studies investigate the contribution of polarimetric signature to the characterization and identification of cancerous tissues. It is a matter of extracting multidimensional polarization images of the physical information which characterize the constituents of the object well beyond the visual information of the intensity images. During this thesis, a Mueller imager, POLARIS, was set up, as well as the appropriate processing and calibration tools. A method of Mueller images segmentation in non-homogeneous illumination has been proposed. A first database of polarimetric multi-spectral images of healthy and pathogenic tissues in mice was constructed. An original approach was finally proposed based on random forests to extract from a set of physical parameters a set of parameters allowing to differentiate the healthy zones from the pathogenic zones at different working wavelengths. A comparison is proposed with the literature and validates the approach.</dcterms:abstract>
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