Research Article
An Experimental Analysis of Fuzzy C-Means and K-Means Segmentation Algorithm for Iron Detection in Brain SWI using Matlab
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
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| Volume 104 - Issue 15 |
| Published: October 2014 |
| Authors: Beshiba Wilson, Julia Punitha Malar Dhas |
10.5120/18281-9250
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Beshiba Wilson, Julia Punitha Malar Dhas . An Experimental Analysis of Fuzzy C-Means and K-Means Segmentation Algorithm for Iron Detection in Brain SWI using Matlab. International Journal of Computer Applications. 104, 15 (October 2014), 36-38. DOI=10.5120/18281-9250
@article{ 10.5120/18281-9250,
author = { Beshiba Wilson,Julia Punitha Malar Dhas },
title = { An Experimental Analysis of Fuzzy C-Means and K-Means Segmentation Algorithm for Iron Detection in Brain SWI using Matlab },
journal = { International Journal of Computer Applications },
year = { 2014 },
volume = { 104 },
number = { 15 },
pages = { 36-38 },
doi = { 10.5120/18281-9250 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2014
%A Beshiba Wilson
%A Julia Punitha Malar Dhas
%T An Experimental Analysis of Fuzzy C-Means and K-Means Segmentation Algorithm for Iron Detection in Brain SWI using Matlab%T
%J International Journal of Computer Applications
%V 104
%N 15
%P 36-38
%R 10.5120/18281-9250
%I Foundation of Computer Science (FCS), NY, USA
Abstract
An accurate assessment of iron accumulation is required for diagnosis and therapy of iron overload in various neurodegenerative diseases. Susceptibility Weighted Imaging (SWI) offers information about any tissue that has a different susceptibility than its surrounding structures. Reliable methods to precisely quantify brain iron are essential. Image segmentation refers to partition of an image into different regions that differ in some characteristics. Accurate segmentation of medical images is a very difficult task. However, the process of accurate segmentation of these images is very important for a correct diagnosis by clinical tools. In this paper, an experimental analysis is done using fuzzy c-means and k-means segmentation algorithm for detection of iron content in SWI brain images.
References
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