Novel local binary textural pattern for analysis and classification of mammogram using support vector machine

Breast cancer is one of the most devastating and deadly diseases for women. It is estimated that between one in eight and one in twelve women will develop breast cancer during their lifetime. The most convenient practical method to detect breast cancer is mammography, because it allows the detection of the cancer at its early stages, a crucial issue for a high survival rate. Mammography is the only technique that has demonstrated the ability to detect breast cancer at an early stage and with high sensitivity and specificity. The search for features in this kind of image is complicated by the higher frequency textural variations in image intensity. In this paper, we have proposed few novel local binary textural patterns for classification of mammogram which was found to have consistent accuracy rate.