DETECTION AND CLASSIFICATION OF MAMMOGRAPHIC CALCIFICATIONS

We propose a detection and classification system for the analysis of mammo-graphic calcifications. First, a new multi-tolerance region growing method is proposed for the detection of potential calcification regions and extraction of their contours. The method employs a distance metric computed on feature sets including measures of shape, centre of gravity, and size obtained for various growth tolerance values in order to determine the most suitable parameters. Then, shape features from moments, Fourier descriptors, and compactness are computed based upon the contours of the regions. Finally, a two-layer perceptron is utilized for the purpose of classification of calcifications with the shape features. A new leave-one-out algorithm-based parameter determination procedure is included in the neural network training step. In our preliminary study, detection rates were 81% and 85±3%, and correct classification rates were 94% and 87% with a test set of 58 benign calcifications and 241±10 malignant calcifications, respectively. The proposed system should provide considerable help to radiologists in the diagnosis of breast cancer.