Artificial neural network based classification of mammographic microcalcifications using image structure features

Mammography associated with clinical breast examination and self-breast examination is the only effective and viable method for mass breast screening. Most of the minimal breast cancers are detected by the presence of microcalcifications. It is however difficult to distinguish between benign and malignant microcalcifications associated with breast cancer. Most of the techniques used in the computerized analysis of mammographic microcalcifications segment the digitized gray-level image into regions representing microcalcifications. Since mammographic images usually suffer from poorly defined microcalcification features, the extraction of microcalcification features based on segmentation process is not reliable and accurate. We present a second-order gray-level histogram based feature extraction approach to extract microcalcification features. These features, called image structure features, are computed from the second-order gray-level histogram statistics, and do not require segmentation of the original image into binary regions. Several image structure features were computed for 100 cases of "difficult to diagnose" microcalcification cases with known biopsy results. These features were analyzed in a correlation study which provided a set of five best image structure features. A feedforward backpropagation neural network was used to classify mammographic microcalcifications using the image structure features. The network was trained on 10 cases of mammographic microcalcifications and tested on additional 85 "difficult-to-diagnose" microcalcifications cases using the selected image structure features. The trained network yielded good results for classification of "difficult-todiagnose" microcalcifications into benign and malignant categories.