An Automated Approach for Segmentation of Brain MR Images using Gaussian Mixture Model based Hidden Markov Random Field with Expectation Maximization

Authors

  • Saurabh A Shah Babaria Institute of Technology, Gujarat Technological University
  • Narendra C Chauhan A D Patel Institute of Technology, Gujarat Technological University

DOI:

https://doi.org/10.14738/jbemi.24.1411

Keywords:

GMM, HMRF, EM, Segmentation, brain tumour

Abstract

Manual segmentation of brain tissues from MR images for diagnosis purpose is time consuming and requires much effort even by experts. This has motivated generation of automated segmentation techniques. Moreover, due to presence of noise in an image and its low contrast, it is difficult to correctly delineate tumour from brain MR images. In this paper, a novel hybrid method using Gaussian Mixture Model based Hidden Markov Random Field (HMRF) with Expectation Maximization (EM) has been proposed which segments tissues from MR brain images efficiently and helps to separate out tumour area easily.  The proposed method minimizes energy function during each iteration of EM and gives comparable results with ground truth. The results obtained are also compared with the results of fuzzy c-means clustering algorithm for image segmentation.

Author Biographies

Saurabh A Shah, Babaria Institute of Technology, Gujarat Technological University

Associate Professor, Department of Computer Science and Engineering

Narendra C Chauhan, A D Patel Institute of Technology, Gujarat Technological University

Professor, Department of Information Technology

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Published

2015-09-02

How to Cite

Shah, S. A., & Chauhan, N. C. (2015). An Automated Approach for Segmentation of Brain MR Images using Gaussian Mixture Model based Hidden Markov Random Field with Expectation Maximization. Journal of Biomedical Engineering and Medical Imaging, 2(4), 57. https://doi.org/10.14738/jbemi.24.1411