Density-Wise Two Stage Mammogram Classification using Texture Exploiting Descriptors
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Breast cancer is becoming pervasive with each passing day. Hence, its early detection is a big step in saving life of any patient. Mammography is a common tool in breast cancer diagnosis. The most important step here is classification of mammogram patches as normal-abnormal and benign-malignant. Texture of a breast in a mammogram patch plays a big role in these classifications. We propose a new feature extraction descriptor called Histogram of Oriented Texture (HOT), which is a combination of Histogram of Gradients (HOG) and a Gabor filter, and exploits this fact. We also revisit the Pass Band Discrete Cosine Transform (PB-DCT) descriptor that captures texture information well. All features of a mammogram patch may not be useful. Hence, we apply a feature selection technique 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 not been studied exhaustively. The Image Retrieval in Medical Application (IRMA) database from RWTH Aachen, Germany is a standard database that provides mammogram patches, and most researchers have tested their frameworks only on a subset of patches from this database. We apply our two new descriptors on all images of the IRMA database for density wise classification, and compare with the standard descriptors. We achieve higher accuracy than all of the existing standard descriptors (more than 92
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