Abnormal mass classification in breast mammography using rotation invariant LBP

We present a novel approach for abnormal breast mass classification from digitized mammography images. The proposed framework exploits rotation invariant uniform Local Binary Pattern (LBP) as texture feature. These features are classified using Support Vector Machine (SVM). In addition, we take advantage of the breast mammograms taken from multiple views or angles. We classify breast scans from `cranial-caudal' view and `mediolateral-oblique' view separately, and combine these classification scores to make an improved diagnosis. This reduces the classification error, and achieves higher recognition rate than that of either views individually. The proposed computer aided diagnosis system was evaluated on DDSM (Digital Database for Screening Mammography) data set, and was able to achieve a classification accuracy of 74%.