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Improving the runtime of MRF based method for MRI brain segmentation

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  • Ahmadvand, Ali
  • Daliri, Mohammad Reza

Abstract

Image segmentation is one of the important parts in medical image analysis. Markov random field (MRF) is one of the successful methods for MRI image segmentation, but conventional MRF methods suffer from high computational cost. MRI images have high level of artifacts such as Partial Volume Effect (PVE), intensity non uniformity (INU) and other noises, so using global optimization methods like simulated annealing (SA) for optimization step is more appropriate than other local optimization methods such as Iterative Conditional Modes (ICM). On the other hand, these methods also has heavy computational burden and they are not appropriate for real time task. This paper uses a proper combination of clustering methods and MRF and proposes a preprocessing step for MRF method for decreasing the computational burden of MRF for segmentation. The results show that the preprocessing step increased the speed of segmentation algorithm by a factor of about 10 and have no large impact on the accuracy of segmentation. Moreover, different clustering methods can be used for the first step and estimation of the parameters. Therefore, using of powerful clustering methods can provide a better segmentation results.

Suggested Citation

  • Ahmadvand, Ali & Daliri, Mohammad Reza, 2015. "Improving the runtime of MRF based method for MRI brain segmentation," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 808-818.
    • Handle: RePEc:eee:apmaco:v:256:y:2015:i:c:p:808-818
    DOI: 10.1016/j.amc.2015.01.053
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    Cited by:

    1. Yu, Zhaofei & Chen, Feng & Dong, Jianwu, 2017. "Neural network implementation of inference on binary Markov random fields with probability coding," Applied Mathematics and Computation, Elsevier, vol. 301(C), pages 193-200.

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