Reconnaissance de formes basée sur l'approche possibiliste dans les images mammographiques

Abstract : In view of the significant increase in breast cancer mortality rate among women as well as the continuous growth in number of mammograms performed each year, computer-aided diagnosis is becoming more and more imperative for experts. In our thesis work, special attention is given to breast masses as they represent the most common sign of breast cancer in mammograms. Nevertheless, mammographic images have very low contrast and breast masses possess ambiguous margins. Thus, it is difficult to distinguish them from the surrounding parenchymal. Moreover, the complexity and the large variability of breast mass shapes make diagnostic and classification challenging tasks.In this context, we propose a computer-aided diagnosis system which firstly segments masses in regions of interests and then classifies them as benign or malignant. Mass segmentation is a critical step in a computer-aided diagnosis system since it affects the performance of subsequent analysis steps namely feature analysis and classification. Indeed, poor segmentation may lead to poor decision making. Such a case may occur due to two types of imperfection: uncertainty and imprecision. Therefore, we propose to deal with these imperfections using fuzzy contours which are integrated in the energy of an active contour to get a fuzzy-energy based active contour model that is used for final delineation of mass.After mass segmentation, a classification method is proposed. This method is based on possibility theory which allows modeling the ambiguities inherent to the knowledge expressed by the expert. Moreover, since shape and margin characteristics are very important for differentiating between benign and malignant masses, the proposed method is essentially based on shape descriptors.The evaluation of the proposed methods was carried out using the regions of interest containing masses extracted from the MIAS base. The obtained results are very interesting and the comparisons made have demonstrated their performances.
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Marwa Hmida. Reconnaissance de formes basée sur l'approche possibiliste dans les images mammographiques. Traitement des images [eess.IV]. Ecole nationale supérieure Mines-Télécom Atlantique, 2017. Français. ⟨NNT : 2017IMTA0061⟩. ⟨tel-01802993⟩

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