Multimedia Tools and Applications

, Volume 77, Issue 7, pp 8001–8018 | Cite as

Segmentation of brain MR images using a proper combination of DCS based method with MRF

  • Ali Ahmadvand
  • Mohammad Reza Daliri
  • Sayyed Mohammadreza Zahiri


Manual segmentation of Magnetic Resonance Images (MRI) is a time-consuming process, thus automatic segmentation of brain MR images has attracted more attention in recent years. In this paper, we introduce Dynamic Classifier Selection Markov Random Field (DCSMRF) algorithm for supervised segmentation of brain MR images into three main tissues such as White Matter (WM), Gray Matter (GM) and Cerebrospinal Fluid (CSF). DCSMRF combines a novel ensemble method with the Markov Random Field (MRF) algorithm and tries to obtain the advantages of both algorithms. For the ensemble part of DCSMRF, we propose an ensemble method called Dynamic Classifier System-Weighted Local Accuracy (DCS-WLA) which is a type of Combination of Multiple Classifier (CMC) algorithm. Later, the MRF algorithm is utilized for incorporating spatial, contextual and textural information in this paper. For the MRF section, an energy function based on the output of the DCS-WLA algorithm is proposed, then maximum value for Maximum A Posterior (MAP) criterion is searched to obtain optimal segmentation. The MRF algorithm applies similar to a post processing step in which only a subset of pixels is selected for optimization step. Hence, a vast amount of search space is pruned. Consequently, the computational burden of the proposed algorithm is more tolerable than the conventional MRF-based methods. Moreover, by employing ensemble algorithms, the accuracy and reliability of final results are enhanced compared to the individual methods.


MRI Ensemble methods Combination of multiple classifier MRF Dynamic classifier system Weighted local accuracy Segmentation 



The work has been supported by internal funding from IUST University. No external financial support has been obtained for this work.

Compliance with ethical standards

Conflict of Interest

The authors have no conflicts of interest to declare


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  • Ali Ahmadvand
    • 1
    • 2
  • Mohammad Reza Daliri
    • 3
  • Sayyed Mohammadreza Zahiri
    • 4
  1. 1.School of Computer EngineeringIran University of Science and Technology (IUST)TehranIran
  2. 2.Mathematics and Computer Science DepartmentEmory UniversityAtlantaUSA
  3. 3.Neuroscience and Neuroengineering Research Lab., Biomedical Engineering Department, School of Electrical EngineeringIran University of Science and Technology (IUST)TehranIran
  4. 4.Department of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaUSA

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