Performance Analysis of Image Segmentation Methods for the Detection of Masses in Mammograms

Abstract

ABSTRACT Detection and quantification of breast cancer is a very critical step in mammograms and therefore, needs an accurate and standard technique for breast tumor segmentation. In the last four decades, a number of algorithms have been published in the literature. Each one has their own merits and demerits. The aim of this paper is to make a comparative analysis of the most promising methods, namely fuzzy c-means (FCM), k-means (KM), marker controlled watershed segmentation (MCWS) and region growing (RG), for the detection and segmentation of masses in mammographic images on real data obtained from Metro Hospital. Robustness of the methods is demonstrated by validating their quantitative results with expert manual data. It is observed that the RG gives better results compared to three other methods. Keywords Breast cancer, mathematical morphology, marker controlled watershed segmentation, region growing. 1. INTRODUCTION Cancer is one of the leading causes of human mortality in the world. The most common type of cancer in women is the breast cancer. Early detection and diagnosis leads to the successful treatment and thus play the key role in controlling the breast cancer deaths. Hence, it is essential for the women of age group 30-40 years to have regular screening every year. Currently, X-ray mammography is considered to be the most simple and reliable imaging method for the early detection of breast cancer [1]. Presence of masses or micro-calcification clusters on mammograms is considered to be preliminary indicators for early stage breast cancer. To determine the tumor area, in most of the hospitals, a radiologist performs the diagnosis of breast tumor manually on mammographic images. Visual examination of large volume of mammograms and shortage of experienced radiologists makes the process error prone and time consuming. Computer-aided diagnosis (CAD) system may help radiologist and doctors in reliable and precise diagnosis of breast cancer [2]. Numerous techniques have been developed and proposed as an emerging tool to segment masses from surrounding tissues in digital mammograms. Martins