||In this thesis, the problem addressed is the development of a computer-aided diagnosis system (CAD) based on conjoint analysis of several images, and therefore on the comparison of these medical images. The particularity of our approach is to look for evolutions or aberrant new tissues in a given set, rather than attempting to characterize, with a strong a priori, the type of tissues. This problem allows to apprehend one aspect of the analysis of a medical file performed by experts which is the study of a case through comparison and evolution detection. The methodology proposed is carried out within the application context of the development of a CAD applied to mammograms. The first step when a couple of images are involved is to perform an adapted registration. Any automated comparison of signals requires an alignment of similar components present on the pictures, that is to say a registration phase, so that they occupy the same space on the two images. As the registration is never perfect, we must take into account the level of uncertainty and develop a comparison method able to distinguish registration error and real small differences between comparable tissues. In many applications, the assessment of similarity used during the registration step is also used in the interpretation step that yields to prompt suspicious regions. In our case, registration is assumed to match the spatial coordinates of similar anatomical elements.
||Advisor: Nicole Vincent. Date and location of PhD thesis defense: 10 January 2013, University of Paris Descartes
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Medical Image Analysis ;
||ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 13, Núm. 2 (2014) , p. 26-28, ISSN 1577-5097