Abstract
Breast cancer is a great threat for women around the world. Mammography is the main approach for early detection and diagnosis. Microcalcification (MC) in mammograms is one of the important early signs of breast cancer. Their accurate detection is important in computer-aided detection (CADe). In this paper, we proposed a new Microcalcification detection method for full field digital mammograms (FFDM) by integrating Possibilistic Fuzzy c-Means (PFCM) clustering algorithm and weighted support vector machine (WSVM). The method includes a training process and a testing process. In the training process, possible microcalcification regions are located and extracted. Extracted features are selected with mutual information based technique. Positive and negative samples are weighted with PFCM and used to train a weighted SVM. A similar procedure is performed on test images. The proposed method is evaluated on a database of 410 clinical mammograms and compared with a standard unweighted support vector machine classifier.
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Acknowledgments
This work is partially supported by the National Natural Science Foundation of China (NO. 61403287, NO. 61273303, NO. 31201121), the Natural Science Foundation of Hubei Province, China (No. 2014CFB288), the Educational Commission of Hubei Proince, China (NO. D20131101), and the China Postdoctoral Science Foundation (NO. 2014M552039).
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Liu, X., Mei, M., Sun, W., Liu, J. (2015). A New Microcalcification Detection Method in Full Field Digital Mammogram Images. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_40
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DOI: https://doi.org/10.1007/978-3-319-22180-9_40
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