International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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Volume 59 - Issue 5 |
Published: December 2012 |
Authors: Neelofar Sohi, Lakhwinder Kaur, Savita Gupta |
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Neelofar Sohi, Lakhwinder Kaur, Savita Gupta . Performance Improvement of Fuzzy C-mean Algorithm for Tumor Extraction in MR Brain Images. International Journal of Computer Applications. 59, 5 (December 2012), 40-45. DOI=10.5120/9547-4000
@article{ 10.5120/9547-4000, author = { Neelofar Sohi,Lakhwinder Kaur,Savita Gupta }, title = { Performance Improvement of Fuzzy C-mean Algorithm for Tumor Extraction in MR Brain Images }, journal = { International Journal of Computer Applications }, year = { 2012 }, volume = { 59 }, number = { 5 }, pages = { 40-45 }, doi = { 10.5120/9547-4000 }, publisher = { Foundation of Computer Science (FCS), NY, USA } }
%0 Journal Article %D 2012 %A Neelofar Sohi %A Lakhwinder Kaur %A Savita Gupta %T Performance Improvement of Fuzzy C-mean Algorithm for Tumor Extraction in MR Brain Images%T %J International Journal of Computer Applications %V 59 %N 5 %P 40-45 %R 10.5120/9547-4000 %I Foundation of Computer Science (FCS), NY, USA
Aim of this paper is to develop an efficient fuzzy c-mean based segmentation algorithm to extract tumor region from MR brain images. First, cluster centroids are initialized through data analysis of tumor region, which optimizes the standard fuzzy c-mean algorithm. Next, reconstruction based morphological operations are applied to enhance its performance for brain tumor extraction. The results show that simple fuzzy c-mean could not segment the region of interest properly, whereas enhanced algorithm effectively extracts the tumor region. From comparison with existing segmentation methods, enhanced fuzzy c-mean algorithm emerges as the most effective algorithm for extracting region of interest.