Classifying Benign and Malignant Mass using GLCM and GLRLM based Texture Features from Mammogram

Mammogram–breast x-ray is considered the most effective, low cost, and reliable method in early detection of breast cancer. Although general rules for the differentiation between benign and malignant breast lesion exist, only 15 to 30% of masses referred for surgical biopsy are actually malignant. In this work, an approach is proposed to develop a computer-aided classification system for cancer detection from digital mammograms. The proposed system consists of three major steps. The first step is region of interest (ROI) extraction of 256×256 pixels size. The second step is the feature extraction; we used a set of 19 GLCM and GLRLM features and the 19 (nineteen) features extracted from grey level run-length matrix and greylevel co-occurrence matrix could distinguishing malignant masses from benign mass with an accuracy 94.9%.Further analysis carried out by involving only 12 of the 19 features extracted, which consists of 5 features extracted from GLCM matrix and 7 features extracted from GLRL matrix. The 12 selected features are: Energy, Inertia, Entropy, Maxprob, Inverse, SRE, LRE, GLN, RLN, LGRE, HGRE, and SRLGE, ARM with 12 features as prediction can distinguish malignant mass image and benign mass with a level of accuracy of 92.3%. Further analysis showing that Area Under the Receiver Operating Curve was 0.995, which means that the accuracy level of classification is good or very good. Based on that data, it concluded that texture analysis based on GLCM and GLRLM could distinguish malignant image and benign image with considerably good result. The third step is the classification process; we used the technique of association rule mining using image content to classify between normal and cancerous mass. The proposed system was shown to have the large potential for cancer detection from digital mammograms