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Image feature analysis and computer‐aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography
Author(s) -
Chan HeangPing,
Doi Kunio,
Galhotra Simranjit,
Vyborny Carl J.,
MacMahon Heber,
Jokich Peter M.
Publication year - 1987
Publication title -
medical physics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.473
H-Index - 180
eISSN - 2473-4209
pISSN - 0094-2405
DOI - 10.1118/1.596065
Subject(s) - mammography , artificial intelligence , computer science , computer vision , feature (linguistics) , digital radiography , digital mammography , computed radiography , pattern recognition (psychology) , feature extraction , image (mathematics) , radiography , image quality , radiology , medicine , breast cancer , linguistics , philosophy , cancer
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.