Evaluating the Efficacy of Second Order Statistical Features for Classification of Mammograms

In the present work, a computer aided classification system has been proposed for classification of mammogram images into normal, benign and cancer classes. The work has been carried out on thirty Digital Database for Screening mammography (DDSM) cases consisting of 10 normal, 10 benign and 10 cancer images. The regions of interest (ROI) have been extracted from the right Medio Lateral Oblique (RMLO) part of the mammogram. We extracted 256×256 pixel size ROI from each case. Texture descriptors based on gray level co-occurrence method by varying the value of inter pixel distance 'd' from 1 to 8 have been used. The SVM classifier has been used for the classification task. The result of the study indicates that GLCM mean and range features computed at d=1 yield the maximum overall classification accuracy of 75% and 65 % respectively.