Textural features based computer aided diagnostic system for mammogram mass classification

Computer Aided Diagnosis (CAD) could be applied as a solution to reduce the chances of human errors and helps Medical Practioners in the correct classification of Breast Masses. This paper emphasizes an algorithm for the early de tection of breast masses. Textural analysis is one of the efficient methods for the early detection of abnormalities. The paper enumerates an efficient Discrete Wavelet Transform (DWT) algorithm and a modified Grey-Level Co-Occurrence Matrix (GLCM) method for textural feature extraction from segmented mammogram images. Each tissue pattern after classification is characterized into Benign and Malignant masses. A total of 148 mammogram images were taken from Mini MIAS database and solid breast nodules were classified into benign and malignant masses using supervised classifiers. The classifier used is Radial Basis Function Neural Network (RBFNN). The proposed system has a high potential for cancer detection from digitized screening mammograms.