Caputo B, La Torre E, Bouattour S, Gigante G E
University of Erlangen-Nurnberg.
Stud Health Technol Inform. 2002;90:30-4.
Mammography associated with clinical breast examination is the only effective method for mass breast screening. Microcalcifications are one of the primary signs for early detection of breast cancer. In this paper we propose a new kernel method for classification of difficult-to-diagnose regions in mammographic images. It consists of a novel class of Markov Random Fields, using techniques developed within the context of statistical mechanics. This method is used for the classification of positive Region of Interest (ROI's) containing clustered microcalcifications and negative ROI's containing normal tissue. The obtained results show that the proposed approach can be successfully employed for detection of microcalcifications
乳腺钼靶检查结合临床乳腺检查是大规模乳腺筛查的唯一有效方法。微钙化是早期发现乳腺癌的主要体征之一。在本文中，我们提出了一种新的核方法，用于对乳腺钼靶图像中难以诊断的区域进行分类。它由一类新颖的马尔可夫随机场组成，采用了在统计力学背景下开发的技术。该方法用于对包含簇状微钙化的阳性感兴趣区域（ROI）和包含正常组织的阴性ROI进行分类。所得结果表明，所提出的方法可成功用于微钙化的检测。
