Breast abnormalities segmentation using the wavelet transform coefficients aggregation

Introduction: Breast cancer is the most common cancer among women in the world. The automatic detection of masses in digital mammograms is a challenging task and a major step in the development of breast cancer CAD systems. In this study, we introduce a new method for automatic detection of suspicious mass candidate (SMC) regions in a mammogram. Methods: Mammography is widely used for the early detection and diagnosis of breast cancer. Extracting the region of interest (ROI) helps to locate the abnormal areas, which may be analyzed further by a radiologist or a CAD system. In this study, we propose a new method for ROI detection in mammography images. After preprocessing the mammogram, an aggregation of discrete wavelet coefficients based on the lifting scheme and the texture characteristics of the mammogram was created. Then, the coefficients were optimized through noise removal and morphological operations, and a canny edge detector was used to segment the mammogram. Finally, to overcome the problem of over segmentation or under segmentation, reduce the false-negative rate, and enhance the detected regions, we used splitting and merging method. The proposed method was evaluated using images from the DDSM database. Results: Sensitivity, , , and FPI were calculated to be 100%, 86.5%, 56%, and 5.4, respectively. Conclusion: Experimental results indicate that the proposed method is able to detect and identify the abnormal regions of the mammogram that are candidates for breast masses. This technique could potentially improve the performance of CAD systems and diagnosis accuracy in mammograms and can be useful for medical staff and students.