Mammogram image visual enhancement, mass segmentation and classification

Abstract

Mammography is the most effective technique for breast cancer screening and detection of abnormalities. However, early detection of breast cancer is dependent on both the radiologist's ability to read mammograms and the quality of mammogram images. In this paper, the researchers have investigated combining several image enhancement algorithms to enhance the performance of breast-region segmentation. The masses that appear in mammogram images are further analyzed and classified into four categories that include: benign, probable benign and possible malignant, probable malignant and possible benign, and malignant. The main contribution of this work is to reveal the optimal combination of various enhancement methods and to segment breast region in order to obtain better visual interpretation, analysis, and classification of mammogram masses to assist radiologists in making more accurate decisions. The experimental dataset consists of a total of more than 1300 mammogram images from both the King Hussein Cancer Center and Jordan Hospital. Results achieved tumor classification accuracy values of 90.7%. Moreover, the results showed a sensitivity of 96.2% and a specificity of 94.4% for the mass classifying algorithm. Radiologists from both institutes have acknowledged the results and confirmed that this work has lead to better visual quality images and that the segmentation and classification of tumors has aided the radiologists in making their diagnoses.