News Updates Monday 24th Oct 2016 :
  • Welcome to International Press Corporation, world's leading publishers, We have served more than 10000+ authors
  • Articles are invited in engineering, science, technology, management, industrial engg, biotechnology etc.
  • Paper submission last date of Sept/Oct 2016 issue is 25 Oct 2016, Submit online or at
  • Our journals are indexed in University of Regensburg Germany, Google Scholar, Cross Reference data bases
  • Applications for reviewers are invited and can be sent directly to concerned editor's mail

MR Brain Image Segmentation based on Markov Random Field with the Application of ACO

Author : R.Helen, N.Kamaraj and R.Vishnupriya

Pages : 388-394
Download PDF


Magnetic resonance (MR) medical image segmentation plays an increasingly important role in computer-aided detection and diagnosis (CAD) of abnormalities. MRI segmentation manually is time consuming and consumes valuable human resources. Hence a great deal of efforts has been made to automate this process. Markov Random Field (MRF) has been one of the most active research areas of MRI brain segmentation which seeks an optimal label field in a large space. The traditional optimization method is Simulated Annealing (SA) that could get the global optimal solution with heavy computation burden. Therefore great deal efforts have been made to obtain the optimal solution in a reasonable time. In this paper, we conduct a comparative study with the traditional minimization approach Simulated Annealing (SA) and a novel proposed method: MRF-Hybrid Parallel Ant Colony Optimization (MRF-HPACO) with Fuzzy C-Means (FCM) Algorithm for the segmentation of MR images. Comparing with Simulated Annealing (SA) and MRF with Improved Genetic Algorithm (MRF-IGA) that is often used in the image segmentations based on Markov Random Field (MRF) models, HPACO has been used in reducing the computation complexity of optimization. There are M colonies, M-1 colonies treated as slaves and one colony for master. Each colonies visit all the pixels without revisit. Initially, initialize the pheromone value for all the colonies. Posterior energy values are computed by Markov Random Field. If this value is less than global minimum, the local minimum is assigned to global minimum. The pheromone of the Ant that generates the global minimum is updated. At the final iteration global minimum returns the optimum threshold value for select the initial clustering the FCM implementation in the brain Magnetic Resonance Image (MRI) segmentation.The qualitative and quantitative results of each system are investigated as well.

Keywords: Ant colony optimisation, Fuzzy C Means algorithm, Image segmentation, Magnetic Resonance Image, Markov Random Field, Simulated Annealing.


Article published in International Journal of Current  Engineering  and Technology, Vol.2,No.4 (Dec- 2012)




Call for Papers
  1. IJCET- Sept/Oct-2016 Issue

    Submission Last Date
    25 Oct 2016
  2. IJTT-Sept-2016
  3. IJAIE-Sept-2016
  4. IJCSB-Sept-2016
  • Inpressco Google Scholar
  • Inpressco Science Central
  • Inpressco Global impact factor
  • Inpressco aap

International Press corporation is licensed under a Creative Commons Attribution-Non Commercial NoDerivs 3.0 Unported License
©2010-2016 INPRESSCO® All Rights Reserved