Abstract

The ranklet transform is a recent image processing technique characterized by a multi–resolution and orientation selective approach similar to that of the wavelet transform. Yet, differently from the latter, it deals with the ranks of the pixels rather than with their gray–level intensity values. In this paper ranklets are used as classification features for a mammographic mass classification problem. Their performances are explored recursively eliminating some of the less discriminant ranklets coefficients according to the cost function of a Support Vector Machine (SVM) classifier. Experiments show good classification performances even after a significant reduction of the number of ranklet coefficients.