Analysis of mammogram classification using a wavelet transform decomposition

Abstract

In order to fully achieve automated mammogram analysis one has to tackle two problems: classification of radial, circumscribed, microcalcifications, and normal samples; and classification of benign, malign, and normal ones. How to extract and select the best features from the images for classification is a very difficult task, since all of those classes are basically irregular textures with a wide visual variety inside each class. Besides there is a lack of tested solutions for these problems in the literature. In this paper we propose to construct and evaluate a supervised classifier for these two problems, by transforming the data of the images in a wavelet basis, and then using special sets of the coefficients as the features tailored towards separating each of those classes. We have realized that this is a suitable solution worth further exploration. For the experiments we have used samples of images labeled by physicians. Results shown are very promising, and the paper describes possible lines for future directions.