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KMID : 0357520090320010045
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Journal of Radiological Science and Technology
2009 Volume.32 No. 1 p.45 ~ p.52
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Development of Automatic Cluster Algorithm for Microcalcification in Digital Mammography
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Choi Seok-Yoon
Kim Chang-Soo
Kim Chang-Soo
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Abstract
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Digital Mammography is an efficient imaging technique for the detection and diagnosis of breast pathological disorders. Six mammographic criteria such as number of cluster, number, size, extent and morphologic shape of microcalcification, and presence of mass, were reviewed and correlation with pathologic diagnosis were evaluated. It is very important to find breast cancer early when treatment can reduce deaths from breast cancer and breast incision. In screening breast cancer, mammography is typically used to view the internal organization. Clusterig microcalcifications on mammography represent an important feature of breast mass, especially that of intraductal carcinoma. Because microcalcification has high correlation with breast cancer, a cluster of a microcalcification can be very helpful for the clinical doctor to predict breast cancer. For this study, three steps of quantitative evaluation are proposed : DoG filter, adaptive thresholding, Expectation maximization. Through the proposed algorithm, each cluster in the distribution of microcalcification was able to measure the number calcification and length of cluster also can be used to automatically diagnose breast cancer as indicators of the primary diagnosis.
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KEYWORD
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Breast cancer, Digital Mammography, Computer-aided detection, Microcalcification, Cluster
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