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Proceedings Paper

Computerized detection and classification of microcalcifications on mammograms
Author(s): Heang-Ping Chan; Datong Wei; Kwok Leung Lam; Shih-Chung Benedict Lo; Berkman Sahiner; Mark A. Helvie M.D.; Dorit D. Adler
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Paper 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 (Az) of 0.91 and 0.89 could be achieved during training, and an Az of 0.82 and 0.87 during jackknife testing for obvious and subtle clusters, respectively. When all cases were combined, the Az was 0.87 and 0.84, respectively, for training and jackknife testing.

Paper Details

Date Published: 12 May 1995
PDF: 9 pages
Proc. SPIE 2434, Medical Imaging 1995: Image Processing, (12 May 1995); doi: 10.1117/12.208734
Show Author Affiliations
Heang-Ping Chan, Univ. of Michigan (United States)
Datong Wei, Univ. of Michigan (United States)
Kwok Leung Lam, Univ. of Michigan (United States)
Shih-Chung Benedict Lo, Georgetown Univ. Medical Ctr. (United States)
Berkman Sahiner, Univ. of Michigan (United States)
Mark A. Helvie M.D., Univ. of Michigan (United States)
Dorit D. Adler, Univ. of Michigan (United States)


Published in SPIE Proceedings Vol. 2434:
Medical Imaging 1995: Image Processing
Murray H. Loew, Editor(s)

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