Computerized detection and classification of microcalcifications on mammograms

Heang-Ping Chan; Datong Wei; Kwok Leung Lam; Shih-Chung Benedict Lo; Berkman Sahiner; Mark A. Helvie M.D.; Dorit D. Adler

doi:10.1117/12.208734

12 May 1995 Computerized detection and classification of microcalcifications on mammograms

Heang-Ping Chan, Datong Wei, Kwok Leung Lam, Shih-Chung Benedict Lo, Berkman Sahiner, Mark A. Helvie M.D., Dorit D. Adler

Author Affiliations +

Proceedings Volume 2434, Medical Imaging 1995: Image Processing; (1995) https://doi.org/10.1117/12.208734
Event: Medical Imaging 1995, 1995, San Diego, CA, United States

Abstract

We are developing computer-aided diagnosis algorithms to assist radiologists in detection and classification of microcalcifications on mammograms. A digitized mammogram was processed with a difference-image technique and signal segmentation methods to identify suspicious signals. False-positive detections were reduced by using morphological features as well as a convolution neural network. A regional clustering technique was applied to the remaining signals to identify clinically significant clustered microcalcifications. For the development of a malignant/benign classifier, the microcalcifications were extracted from the digital images by computerized segmentation techniques. A number of visibility descriptors and shape descriptors were developed to describe the features of the microcalcifications. Linear discriminant analysis and receiver operating characteristic (ROC) methodology were used to classify the benign and malignant microcalcifications. For detection of microcalcifications, the computer reached a true-positive (TP) rate of 100% at 0.1 false-positive (FP) clusters per image for obvious microcalcifications, a TP rate of 93% at 1 FP clusters per image for average subtle microcalcifications, and a TP rate of 87% at 1.5 FP clusters per image for very subtle microcalcifications. For classification of microcalcifications, preliminary results indicated that an area under the ROC curve (A_z) of 0.91 and 0.89 could be achieved during training, and an A_z of 0.82 and 0.87 during jackknife testing for obvious and subtle clusters, respectively. When all cases were combined, the A_z was 0.87 and 0.84, respectively, for training and jackknife testing.

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Heang-Ping Chan, Datong Wei, Kwok Leung Lam, Shih-Chung Benedict Lo, Berkman Sahiner, Mark A. Helvie M.D., and Dorit D. Adler "Computerized detection and classification of microcalcifications on mammograms", Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995); https://doi.org/10.1117/12.208734

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