Image feature analysis and computer-aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography.
We have investigated the application of computer-based methods to the detection of microcalcifications in digital mammograms. The computer detection system is based on a difference-image technique in which a signal-suppressed image is subtracted from a signal-enhanced image to remove the structured background in a mammogram. Signal-extraction techniques adapted to the known physical characteristics of microcalcifications are then used to isolate microcalcifications from the remaining noise background. We employ Monte Carlo methods to generate simulated clusters of microcalcifications that are superimposed on normal mammographic backgrounds. This allows quantitative evaluation of detection accuracy of the computer method and the dependence of this accuracy on the physical characteristics of the microcalcifications. Our present computer method can achieve a true-positive cluster detection rate of approximately 80% at a false-positive detection rate of one cluster per image. The potential application of such a computer-aided system to mammographic interpretation is demonstrated by its ability to detect microcalcifications in clinical mammograms.
D000465:Algorithms; D001322:Autoanalysis; D001943:Breast Neoplasms / Q000150:complications / Q000000981:diagnostic imaging*; D002114:Calcinosis / Q000150:complications / Q000000981:diagnostic imaging*; D003201:Computers; D005260:Female; D006801:Humans; D008327:Mammography / Q000295:instrumentation / Q000379:methods*; D009010:Monte Carlo Method