An Optimized Feature Selection Method For Breast Cancer Diagnosis in Digital Mammogram using Multiresolution Representation

This paper introduces a method for feature extraction from multiresolution representations (wavelet,curvelet) for classification of digital mammograms. The proposed method selects the features according to its capability to distinguish between different classes. The method starts with both performing wavelet and curvelet transform over mammogram images. The resulting coefficients of each image are used to construct a matrix. Each row in the matrix corresponds to an image. The most significant features, in terms of capabilities of differentiating classes, are selected. The method uses threshold values to select the columns that will maximize the difference between the different classes'representatives. The proposed method is applied to the mammographic image analysis society (MIAS) dataset. The results calculated using 2x5-folds cross validation show that the proposed method is able to find an appropriate feature set that lead to significant improvement in classification accuracy. The obtained results were satisfactory and the performances of both wavelet and curvelet are presented and compared.