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Segmentation of Brain MR Images based on Finite Skew Gaussian Mixture Model with Fuzzy C-Means Clustering and EM Algorithm

by Nagesh Vadaparthi, Srinivas Yarramalle, Suresh Varma Penumatsa, P.S.R.Murthy
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 28 - Number 10
Year of Publication: 2011
Authors: Nagesh Vadaparthi, Srinivas Yarramalle, Suresh Varma Penumatsa, P.S.R.Murthy
10.5120/3427-4782

Nagesh Vadaparthi, Srinivas Yarramalle, Suresh Varma Penumatsa, P.S.R.Murthy . Segmentation of Brain MR Images based on Finite Skew Gaussian Mixture Model with Fuzzy C-Means Clustering and EM Algorithm. International Journal of Computer Applications. 28, 10 ( August 2011), 18-26. DOI=10.5120/3427-4782

@article{ 10.5120/3427-4782,
author = { Nagesh Vadaparthi, Srinivas Yarramalle, Suresh Varma Penumatsa, P.S.R.Murthy },
title = { Segmentation of Brain MR Images based on Finite Skew Gaussian Mixture Model with Fuzzy C-Means Clustering and EM Algorithm },
journal = { International Journal of Computer Applications },
issue_date = { August 2011 },
volume = { 28 },
number = { 10 },
month = { August },
year = { 2011 },
issn = { 0975-8887 },
pages = { 18-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume28/number10/3427-4782/ },
doi = { 10.5120/3427-4782 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:14:26.116870+05:30
%A Nagesh Vadaparthi
%A Srinivas Yarramalle
%A Suresh Varma Penumatsa
%A P.S.R.Murthy
%T Segmentation of Brain MR Images based on Finite Skew Gaussian Mixture Model with Fuzzy C-Means Clustering and EM Algorithm
%J International Journal of Computer Applications
%@ 0975-8887
%V 28
%N 10
%P 18-26
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Segmentation is a process of converting inhomogeneous data into homogeneous data. There are many segmentation techniques available inthe literature. Among these techniques, finite Gaussian Mixture Model using EM algorithm is one mostly used. However, Gaussian Mixture Model is suited well when the image under consideration is symmetric. But in reality, medical images are asymmetric. Hence, it is needed to develop new algorithms for segmenting non – symmetric images. Therefore, skew symmetric mixture model is utilized for this purpose. The segmentation is carried out by using Fuzzy C-Means clustering technique and the updated parameters are obtained through EM algorithm. The model is tested with 8 images and the segmentation evaluation is carried out by using objective evaluation criteria namely Jaccard Coefficient (JC) and Volumetric Similarity (VS), Variation of Information (VOI), Global Consistency Error (GCE) and Probabilistic Rand Index (PRI). The performance evaluation of reconstructed images is carried out by using image quality metrics. The experimentation is carried out using T1 weighted images and the results are compared with the existing models.

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Index Terms

Computer Science
Information Sciences

Keywords

Segmentation Skew Gaussian Mixture Model Objective Evaluation Image Quality Metrics EM algorithm

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