Computer-aided detection and diagnosis of masses and clustered microcalcifications from digital mammograms

Robert M. Nishikawa; Maryellen Lissak Giger; Kunio Doi; Carl J. Vyborny; Robert A. Schmidt; Charles E. Metz; Chris Yuzheng Wu; Fang-Fang Yin; Yulei Jiang; Zhimin Huo; Ping Lu; Wei Zhang; Takahiro Ema; Ulrich Bick; John Papaioannou; Rufus H. Nagel

doi:10.1117/12.148655

29 July 1993 Computer-aided detection and diagnosis of masses and clustered microcalcifications from digital mammograms

Robert M. Nishikawa, Maryellen Lissak Giger, Kunio Doi, Carl J. Vyborny, Robert A. Schmidt, Charles E. Metz, Chris Yuzheng Wu, Fang-Fang Yin, Yulei Jiang, Zhimin Huo, Ping Lu, Wei Zhang, Takahiro Ema, Ulrich Bick, John Papaioannou, Rufus H. Nagel

Author Affiliations +

Proceedings Volume 1905, Biomedical Image Processing and Biomedical Visualization; (1993) https://doi.org/10.1117/12.148655
Event: IS&T/SPIE's Symposium on Electronic Imaging: Science and Technology, 1993, San Jose, CA, United States

Abstract

We are developing an 'intelligent' workstation to assist radiologists in diagnosing breast cancer from mammograms. The hardware for the workstation will consist of a film digitizer, a high speed computer, a large volume storage device, a film printer, and 4 high resolution CRT monitors. The software for the workstation is a comprehensive package of automated detection and classification schemes. Two rule-based detection schemes have been developed, one for breast masses and the other for clustered microcalcifications. The sensitivity of both schemes is 85% with a false-positive rate of approximately 3.0 and 1.5 false detections per image, for the mass and cluster detection schemes, respectively. Computerized classification is performed by an artificial neural network (ANN). The ANN has a sensitivity of 100% with a specificity of 60%. Currently, the ANN, which is a three-layer, feed-forward network, requires as input ratings of 14 different radiographic features of the mammogram that were determined subjectively by a radiologist. We are in the process of developing automated techniques to objectively determine these 14 features. The workstation will be placed in the clinical reading area of the radiology department in the near future, where controlled clinical tests will be performed to measure its efficacy.

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Robert M. Nishikawa, Maryellen Lissak Giger, Kunio Doi, Carl J. Vyborny, Robert A. Schmidt, Charles E. Metz, Chris Yuzheng Wu, Fang-Fang Yin, Yulei Jiang, Zhimin Huo, Ping Lu, Wei Zhang, Takahiro Ema, Ulrich Bick, John Papaioannou, and Rufus H. Nagel "Computer-aided detection and diagnosis of masses and clustered microcalcifications from digital mammograms", Proc. SPIE 1905, Biomedical Image Processing and Biomedical Visualization, (29 July 1993); https://doi.org/10.1117/12.148655

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