Digital Characterization Of Clinical Mammographic Microcalcifications: Applications In Computer-Aided Detection

Heang-Ping Chan; Kunio Doi; Kwok-Leung Lam; Carl J. Vyborny; Robert A. Schmidt; Charles E. Metz

doi:10.1117/12.968685

27 June 1988 Digital Characterization Of Clinical Mammographic Microcalcifications: Applications In Computer-Aided Detection

Heang-Ping Chan, Kunio Doi, Kwok-Leung Lam, Carl J. Vyborny, Robert A. Schmidt, Charles E. Metz

Author Affiliations +

Proceedings Volume 0914, Medical Imaging II; (1988) https://doi.org/10.1117/12.968685
Event: Medical Imaging II, 1988, Newport Beach, CA, United States

Abstract

As part of our ongoing effort to develop an automated computer scheme for the detection and analysis of microcalcifications in digital mammograms, we have analyzed the physical characteristics of microcalcifications from a data base of 39 clinical mammograms in patients undergoing breast biopsy. A signal-extraction method was developed for determination of the size, contrast, and signal-to-noise ratio (SNR) of each microcalcification from unprocessed mammograms. The average power spectrum of the microcalcifications thus extracted was compared to that of the mammographic background. Based on an analysis of these characteristics, we designed a new type of spatial filter, obtained as the difference between a matched filter and a box-rim filter, that can selectively preserve the frequency content of microcalcifications while suppressing the low-frequency background and high-frequency noise. The SNR of the microcalcifications is thereby enhanced. Signal-extraction tests that make use of the size, contrast, local frequency content, and clustering properties of microcalcifications were employed for further discrimination between true signals and normal mammographic structures or artifacts. In order to evaluate the potential clinical utility of our approach, we applied the program to 20 clinical mammograms that contained subtle clustered microcalcifications. These mammograms were not included in the data base mentioned above. The automated computer detection scheme provided a true-positive cluster detection rate of 90% at a false-positive detection rate of one-half cluster per image. These results demonstrate the feasibility of using computer methods to aid radiologists in screening of mammograms for subtle microcalcifications.

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Heang-Ping Chan, Kunio Doi, Kwok-Leung Lam, Carl J. Vyborny, Robert A. Schmidt, and Charles E. Metz "Digital Characterization Of Clinical Mammographic Microcalcifications: Applications In Computer-Aided Detection", Proc. SPIE 0914, Medical Imaging II, (27 June 1988); https://doi.org/10.1117/12.968685

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