Pectoral Muscles Removal in Mammogram Image by Hybrid Bounding Box and Region Growing Algorithm

Breast cancer is one of the most common causes of death among women globally. Accurate and early detection is necessary for decreasing mortality and increase treatment success rates. Mammogram image is currently one of the best ways to detect breast cancer in the early stages, but it contains many artifacts such as noise, labels, and pectoral muscles, that must be deleted or suppressed because it greatly affects the results of the diagnosis in the coming stages. Removing the pectorals muscle is the biggest problem because it possesses an intensity tissue that closely resembles the tissue of fat, glands, and tumors in the form of mammograms. In this paper, an effective algorithm has been suggested by Hybridization Bounding Box and Region growing algorithm (HBBRG) algorithm to solve the problem of pectoral muscle removal which greatly affects the results of tumor detection in the next stages by combines the Bounding Box (BB) and Region growing (RG). To perform this work, pre-processing for mammogram images was applied in two stages. In the first stage, a medium filter and binary image with a specific threshold were used to remove noise and label respectively. In the second phase, the pectoral muscles were removed by applying the (BB) and (RG) algorithm separately, and then we proposed merging the two methods to set up an HBBRG algorithm with the aim to get better results for remove pectoral muscles. The proposed algorithms were tested on all the Mammographic Image Analysis Society (MIAS) database images, and the results showed a significant advantage in the HBBRG algorithm compared to other algorithms as it achieved results in over 98% to completely remove the pectoral muscles of all types of images.