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Reseach Article

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

by Zainul Abdin Jaffery, Zaheeruddin, Laxman Singh
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

@article{ 10.5120/14092-2100,
author = { Zainul Abdin Jaffery, Zaheeruddin, Laxman Singh },
title = { Performance Analysis of Image Segmentation Methods for the Detection of Masses in Mammograms },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 82 },
number = { 2 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 44-50 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume82/number2/14092-2100/ },
doi = { 10.5120/14092-2100 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:57:17.503385+05:30
%A Zainul Abdin Jaffery
%A Zaheeruddin
%A Laxman Singh
%T Performance Analysis of Image Segmentation Methods for the Detection of Masses in Mammograms
%J International Journal of Computer Applications
%@ 0975-8887
%V 82
%N 2
%P 44-50
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
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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Index Terms

Computer Science
Information Sciences

Keywords

Breast cancer mathematical morphology marker controlled watershed segmentation region growing.