The week's pick
Reseach Article
Performance Analysis of Image Segmentation Methods for the Detection of Masses in Mammograms
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 82 - Number 2 |
| Year of Publication: 2013 |
| Authors: Zainul Abdin Jaffery, Zaheeruddin, Laxman Singh |
10.5120/14092-2100
|
Zainul Abdin Jaffery, Zaheeruddin, Laxman Singh . Performance Analysis of Image Segmentation Methods for the Detection of Masses in Mammograms. International Journal of Computer Applications. 82, 2 ( November 2013), 44-50. DOI=10.5120/14092-2100
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.
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