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A hybrid harmony search algorithm for MRI brain segmentation

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Abstract

Automatic magnetic resonance imaging (MRI) brain segmentation is a challenging problem that has received significant attention in the field of medical image processing. In this paper, we present a new dynamic clustering algorithm based on the hybridization of harmony search (HS) and fuzzy c-means to automatically segment MRI brain images in an intelligent manner. In our algorithm, the capability of standard HS is modified to automatically evolve the appropriate number of clusters as well as the locations of cluster centers. By incorporating the concept of variable length encoding in each harmony memory vector, this algorithm is able to represent variable numbers of candidate cluster centers at each iteration. A new HS operator, called the “empty operator”, has been introduced to support the selection of empty decision variables in the harmony memory vector. The PBMF cluster validity index is used as an objective function to validate the clustering result obtained from each harmony memory vector. Evaluation of the proposed algorithm has been performed using both real MRI data obtained from the Center for Morphometric Analysis at Massachusetts General Hospital and simulated MRI data generated using the McGill University BrainWeb MRI simulator. Experimental results show the ability of this algorithm to find the appropriate number of naturally occurring regions in brain images. Furthermore, the superiority of the proposed algorithm over various state-of-the-art segmentation algorithms is demonstrated quantitatively.

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Acknowledgments

Many thanks to the anonymous reviewers for their valuable comments that helped to improve this paper. This research is supported by Universiti Sains Malaysia, USM’s fellowship scheme and 'Universiti Sains Malaysia Research University Grant’ grant titled 'Delineation and visualization of Tumour and Risk Structures—DVTRS' under grant number 1001 / PKOMP / 817001.

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Authors and Affiliations

  1. Computer Vision Research Group, School of Computer Sciences, University Sains Malaysia, 11800 USM, Penang, Malaysia

    Osama Moh’d Alia & Rajeswari Mandava

  2. Department of Radiology-Health Campus, Universiti Sains Malaysia, 16150, Kubang Kerian, Kelantan, Malaysia

    Mohd Ezane Aziz

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  1. Osama Moh’d Alia
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  2. Rajeswari Mandava
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  3. Mohd Ezane Aziz
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Correspondence to Osama Moh’d Alia.

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Alia, O.M., Mandava, R. & Aziz, M.E. A hybrid harmony search algorithm for MRI brain segmentation. Evol. Intel. 4, 31–49 (2011). https://doi.org/10.1007/s12065-011-0048-1

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