Radiology, Vol 198, 671-678, Copyright © 1996 by Radiological Society of North America

ARTICLES

Malignant and benign clustered microcalcifications: automated feature analysis and classification

Y Jiang, RM Nishikawa, DE Wolverton, CE Metz, ML Giger, RA Schmidt, CJ Vyborny and K Doi
Department of Radiology, University of Chicago, Illinois 60637, USA.

PURPOSE: To develop a method for differentiating malignant from benign clustered microcalcifications in which image features are both extracted and analyzed by a computer. MATERIALS AND METHODS: One hundred mammograms from 53 patients who had undergone biopsy for suspicious clustered microcalcifications were analyzed by a computer. Eight computer-extracted features of clustered microcalcifications were merged by an artificial neural network. Human input was limited to initial identification of the microcalcifications. RESULTS: Computer analysis allowed identification of 100% of the patients with breast cancer and 82% of the patients with benign conditions. The accuracy of computer analysis was statistically significantly better than that of five radiologists (P = .03). CONCLUSION: Quantitative features can be extracted and analyzed by a computer to distinguish malignant from benign clustered microcalcifications. This technique may help radiologists reduce the number of false-positive biopsy findings.

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