Purpose: It is difficult for radiologists to make correct clinical decisions for biopsy or follow-up on clustered microcalcifications on mammograms by taking into account possible histological classifications. The purpose of this study is to develop an automated computerized scheme for identifying histological classification of clustered microcalcifications on mammograms in order to assist radiologists' interpretation as a "second opinion".
Methods and Materials: Our database consisted of 58 magnification mammograms (512x512pixels, 12bit/pixels, 0.025mm/pixel), which included 35 malignant clustered microcalcifications (invasive carcinoma, noninvasive carcinoma of comedo type, and noninvasive carcinoma of noncomedo type) and 23 benign clustered microcalcifications (mastopathy and fibroadenoma). The histological classification of all clusters were proved by pathological diagnosis. The clustered microcalcifications were first segmented by using a filter bank and a thresholding technique. Five objective features on clustered microcalcifications were determined by taking into account subjective features with which radiologists commonly use to estimate the likelihood of malignancy. These features were: (1)the variation in the size of microcalcifications within a cluster, (2)the variation in pixel values of microcalcifications within a cluster, (3)the irregularity in the shape of microcalcifications within a cluster, (4)the extent of linear and branching distribution of microcalcifications, and (5)the distribution of microcalcifications in the direction toward nipple. Bayes decision rule with five features was employed for distinguishing between five histological classifications.
Results: The sensitivity and the specificity of this computerized scheme for distinction between malignant and benign clustered microcalcifications were 97.1%(34/35) and 95.7%(22/23), respectively. The sensitivity for distinguishing between three abnormal histological classifications was 77.8%(7/9) for invasive carcinoma, 75.0%(9/12) for noninvasive carcinoma of comedo type, 92.9%(13/14) for noninvasive carcinoma of noncomedo type. The specificity for distinguishing between two benign histological classifications was 94.1%(16/17) for mastopathy, and 100.0%(6/6) for fibroadenoma.
Conclusion: This automated computerized scheme may be useful to assist radiologists in their assessment of clustered microcalcification. (S.K, K.D. are shareholders in R2 Technology. K.D is shareholder in Deus Technologies.)
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