DOI: 10.36347/sjams.2020.v08i10.004

Pages: 2231-2235

This study concern to characterize the breast masses in mammography were defining the breast tissues to tumor, gland, fat and connective tissue, at cancer diagnostic medical center and carried out using Interactive Data Language [IDL] program as platform for the generated codes. The texture analysis technique used to find the gray level variation in mammography images. Analyzing the image with Interactive Data Language IDL software to measure the grey level variation of mammography images. The result of the classification showed that the breast areas were classified well from the rest of the tissues although it has characteristics mostly similar to surrounding tissue. Several texture features are introduced from first order statistics and the classification score matrix generated by linear discriminate analysis and the classification accuracy of breast tissues classified to Tumor 96.8%, gland 57.9%, fat 98.9, While the connective tissue showed a classification accuracy 98.5%. The overall classification accuracy of breast area 94.0%. These relationships are stored in a Texture Dictionary that can be later used to automatically annotate new mammography images with the appropriate breast area names.