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Number 2

Abnormality Detection of Brain MR Image Segmentation using Iterative Conditional Mode Algorithm

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  • A. Ramaswamy Reddy and E. V. Prasad and L. S. S. Reddy 2013. Abnormality Detection of Brain MR Image Segmentation using Iterative Conditional Mode Algorithm. International Journal of Applied Information Systems. 5, 2 (January 2013), 56-66. DOI=http://dx.doi.org/10.5120/ijais450767 
  • @article{10.5120/ijais2017451568,
author = {A. Ramaswamy Reddy and E. V. Prasad and L. S. S. Reddy},
title = {Abnormality Detection of Brain MR Image Segmentation using Iterative Conditional Mode Algorithm},
journal = {International Journal of Applied Information Systems},
issue_date = {January 2013},
volume = {5},
number = {},
month = {January},
year = {2013},
issn = {},
pages = {56-66},
numpages = {},
url = {/archives/volume5/number2/421-0767},
doi = { 10.5120/ijais12-450767},
publisher = { xA9 2012 by IJAIS Journal},
address = {}
}

  • %1 450767
%A A.  Ramaswamy Reddy
%A E.  V.  Prasad
%A L.  S.  S.  Reddy
%T Abnormality Detection of Brain MR Image Segmentation using Iterative Conditional Mode Algorithm
%J International Journal of Applied Information Systems
%@ 
%V 5
%N 
%P 56-66
%D 2013
%I  xA9 2012 by IJAIS Journal

Abstract

In medical image processing, Brain MR Image segmentation is a typical problem for researcher to extract information without loss of details with good resolution. In this paper, we propose a novel method of segmentation using Iterative Conditional Model (ICM) algorithm and Markov random field (MRF) model to detect the abnormality in MR images. The lowest energy label making is allowed by ICM and processed for all iterations. This method supports high compressed relation between label and boundary MRFs. The study of steadily takes will consider all conditions of a discontinues (single edge) existing in a 3 X 3 kernel also including problematical prior information about the interaction between label and boundary. The model is tested with 5 images and the segmentation evaluation is carry 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 segmented images is carried out by using image quality metrics. The simulated results proposed by using T1 weighted images are compared with the existing models.

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Keywords

Brain MR, Iterative conditional mode, Markov Random field, Image segmentation, Kernel, Quality metrics

Index Terms

Computer Science
Information Sciences