An Application of Automatic Classification Scheme for Mammograms Based on the Assessment of Fibroglandular Breast Tissue Density to Abnormal Cases

概要

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it is very important to assess fibroglandular breast tissue density to define the degree of the risk that the lesions are obscured by normal breast tissue. We developed an automated classification method, in which the mammograms were divided into three regions by both the variance histogram analysis and discriminant analysis and were classified into four categories based on the ratios of each of three regions. The classification results by the normal images showed the high agreement rate between physicians and computer. In this study, we applied this scheme to abnormal cases. The assessment of fibroglandular breast tissue density was not influenced by calcifications. As a result of malignant images' classification, the influence of existence of mass regions to classification results is dependent on not only mass sizes but also these positions. Because the rate of different classifications of right and left images in malignant massdatabase is larger than that in normal one, it may be possible to apply this scheme for potential indication of the detection of mass lesions.
Medical Imaging and Information Sciencesの論文