Computerized detection and classification of microcalcifications on mammograms
Heang‐Ping Chan, Datong Wei, Kwok Leung Lam, Shih‐Chung B. Lo, Berkman Sahiner, Mark A. Helvie, Dorit D. Adler · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
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.