Automated detection of clustered microcalcifications in digital mammograms using wavelet processing techniques

Hiroyuki Yoshida; Kunio Doi; Robert M. Nishikawa

doi:10.1117/12.175126

11 May 1994 Automated detection of clustered microcalcifications in digital mammograms using wavelet processing techniques

Hiroyuki Yoshida, Kunio Doi, Robert M. Nishikawa

Author Affiliations +

Proceedings Volume 2167, Medical Imaging 1994: Image Processing; (1994) https://doi.org/10.1117/12.175126
Event: Medical Imaging 1994, 1994, Newport Beach, CA, United States

Abstract

A computerized scheme for the automated detection of clustered microcalcifications in digital mammograms is being developed. This scheme is part of an overall package for computer-aided diagnosis (CAD), the purpose of which is to assist radiologists in detecting and diagnosing breast cancer. One important step in the computer detection scheme is to increase the signal-to-noise ratio of microcalcifications by suppressing the background structure of the breast image in order to increase the sensitivity and/or to reduce the false-positive rate. To achieve this, we employ an approach using the wavelet transform in this paper. Digitized mammograms are decomposed by using the wavelt transform and then reconstructed from transform coefficients modified at several levels in the transform space. Various types of wavelets are examined, and the Least Asymmetric Daubechies' wavelets are chosen to detect clustered microcalcifications in mammograms. The images reconstructed from several different scales are subjected to our CAD scheme, and their performances are evaluated using 39 mamograms containing 41 clusters. Preliminary results show a sensitvity of appromimately 85 percent with a flase-positive rate of 5 clusters per image.

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Hiroyuki Yoshida, Kunio Doi, and Robert M. Nishikawa "Automated detection of clustered microcalcifications in digital mammograms using wavelet processing techniques", Proc. SPIE 2167, Medical Imaging 1994: Image Processing, (11 May 1994); https://doi.org/10.1117/12.175126

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