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Automatic MR Brain Tumor Detection using Possibilistic C-Means and K-Means Clustering with Color Segmentation

by Hema Rajini.n, Bhavani.r
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 56 - Number 2
Year of Publication: 2012
Authors: Hema Rajini.n, Bhavani.r
10.5120/8862-2825

Hema Rajini.n, Bhavani.r . Automatic MR Brain Tumor Detection using Possibilistic C-Means and K-Means Clustering with Color Segmentation. International Journal of Computer Applications. 56, 2 ( October 2012), 11-17. DOI=10.5120/8862-2825

@article{ 10.5120/8862-2825,
author = { Hema Rajini.n, Bhavani.r },
title = { Automatic MR Brain Tumor Detection using Possibilistic C-Means and K-Means Clustering with Color Segmentation },
journal = { International Journal of Computer Applications },
issue_date = { October 2012 },
volume = { 56 },
number = { 2 },
month = { October },
year = { 2012 },
issn = { 0975-8887 },
pages = { 11-17 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume56/number2/8862-2825/ },
doi = { 10.5120/8862-2825 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:57:50.081908+05:30
%A Hema Rajini.n
%A Bhavani.r
%T Automatic MR Brain Tumor Detection using Possibilistic C-Means and K-Means Clustering with Color Segmentation
%J International Journal of Computer Applications
%@ 0975-8887
%V 56
%N 2
%P 11-17
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Magnetic resonance imaging is often the medical imaging method of choice when soft-tissue delineation is necessary. This paper presents a new approach for automated detection of brain tumor based on k-means and possibilistic c-means clustering with color segmentation, which separates brain tumor from healthy tissues in magnetic resonance images. The magnetic resonance feature images used for the tumor detection consist of T1-weighted and T2-weighted images for each axial slice through the head. The proposed method consists of three stages namely pre-processing, segmentation and feature extraction. In the first stage, we have suppressed the noise using image filtering. In the second stage, segmentation is computed using an unsupervised k-means and possibilistic c-means clustering algorithm with color conversion. The segmentation accuracy is obtained using the silhouette method. The experimental results show the superiority of the possibilistic c-means clustering method. In the third stage, the key features are extracted using the threshold. The application of the proposed method for tracking tumor is demon¬strated to help pathologists distinguish exactly tumor size and region.

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Index Terms

Computer Science
Information Sciences

Keywords

Magnetic Resonance Imaging (MRI) K-means clustering Segmentation Possibilistic C-means clustering Tumor

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