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Breast cancer is the most commonly diagnosed and the second leading cause of cancer death among women. In this paper we have proposed a multi stage system for detection of microcalcification using adaptive algorithms. Conventional image processing techniques do not perform well on mammographic images. The large variation in feature size and shape reduces the effectiveness of classical fixed neighborhood...
A mainstay in cancer diagnostics is the classification or grading of cell nuclei based on their appearance. While the analysis of cytological samples has been automated successfully for a long time, the complexity of histological tissue samples has prevented a reliable classification with machine vision techniques. We approach this complex problem in multiple stages, analyzing first image quality,...
Mass detection in mammograms is a challenging problem. In this paper, we propose a cost-sensitive cascaded method for automatic mass detection, which employs machine learning techniques to detect region of interests (ROI). In detail, we divide the original mammograms into overlapped squared sub-images. For each sub-image, intensity features based on gray histogram, texture features based on spatial...
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