Automated multimodal segmentation of an abnormal breast mass in mammogram.

A lot of computer-aided diagnosis systems have been attempted to segment automatically breast mass from a mammogram and to classify the mass as benign and malignant quantitatively. This study aimed to develop an automated computer-aided diagnosis system to evaluate the disease with high accuracy using the proposed multimodal segmentation algorithm when compared to an abnormal breast mass region outlined in mammogram by radiologists of American College of Radiology as "standard." In this study, a total number of 150 mammograms were downloaded from the DDSM database for screening mammography. Based on the available diagnostic report, the studied data were classified as follows: (1) Group I: normal (n = 50, mean ± SD age = 55 ± 8 years), (2) Group II: benign breast cancer (n = 50, mean ± SD age = 58 ± 11 years), and (3) Group III: malignant breast cancer (n = 50, mean ± SD age = 58±9 years). It was found that the proposed multimodal segmentation algorithm processed all the mammograms of different mass types, density, shapes, size, margin, calcification type, and distortion successfully, and it segmented the mass automatically with high accuracy. In this study, a computer-aided diagnosis system was developed to segment the breast mass automatically in a mammogram with high accuracy of 96%. The sensitivity and specificity of the system were found to be 94% and 97%, respectively, when compared to abnormal region outlined in mammogram by radiologists of American College of Radiology as standard.