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Breast cancer is becoming pervasive with each passing day. Hence, its earlydetection is a big step in saving the life of any patient. Mammography is acommon tool in breast cancer diagnosis. The most important step here isclassification of mammogram patches as normal-abnormal and benign-malignant.  Texture of a breast in a mammogram patch plays a significant role in theseclassifications. We propose a variation of Histogram of Gradients (HOG) andGabor filter combination called Histogram of Oriented Texture (HOT) thatexploits this fact. We also revisit the Pass Band - Discrete Cosine Transform(PB-DCT) descriptor that captures texture information well. All features of amammogram patch may not be useful. Hence, we apply a feature selectiontechnique called Discrimination Potentiality (DP). Our resulting descriptors,DP-HOT and DP-PB-DCT, are compared with the standard descriptors.  Density of a mammogram patch is important for classification, and has notbeen studied exhaustively. The Image Retrieval in Medical Application (IRMA)database from RWTH Aachen, Germany is a standard database that providesmammogram patches, and most researchers have tested their frameworks only on asubset of patches from this database. We apply our two new descriptors on allimages of the IRMA database for density wise classification, and compare withthe standard descriptors. We achieve higher accuracy than all of the existingstandard descriptors (more than 92%).

Density-Wise Two Stage Mammogram Classification using Texture Exploiting Descriptors