Deformable object segmentation in ultra-sound images

Abstract : Breast cancer is the second most common type of cancer being the leading cause of cancer death among females both in western and in economically developing countries. Medical imaging is key for early detection, diagnosis and treatment follow-up. Despite Digital Mammography (DM) remains the reference imaging modality, Ultra-Sound (US) imaging has proven to be a successful adjunct image modality for breast cancer screening, specially as a consequence of the discriminative capabilities that US offers for differentiating between solid lesions that are benign or malignant. Despite US usability,US suffers inconveniences due to its natural noise that compromises the diagnosis capabilities of radiologists. Therefore the research interest in providing radiologists with Computer Aided Diagnosis (CAD) tools to assist the doctors during decision taking. This thesis analyzes the current strategies to segment breast lesions in US data in order to infer meaningful information to be feet to CAD, and proposes a fully automatic methodology for generating accurate segmentations of breast lesions in US data with low false positive rates.
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Joan Massich. Deformable object segmentation in ultra-sound images. Signal and Image processing. Université de Bourgogne, 2013. English. ⟨NNT : 2013DIJOS090⟩. ⟨tel-01150950⟩

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