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Automatic MR brain image segmentation using a multiseed based multiobjective clustering approach

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Abstract

In this paper, the automatic segmentation of a multispectral magnetic resonance image of the brain is posed as a clustering problem in the intensity space. The automatic clustering problem is thereafter modelled as solving a multiobjective optimization (MOO) problem, optimizing a set of cluster validity indices simultaneously. A multiobjective clustering technique, named MCMOClust, is used to solve this problem. MCMOClust utilizes a recently developed simulated annealing based multiobjective optimization method as the underlying optimization strategy. Each cluster is divided into several small hyperspherical subclusters and the centers of all these small sub-clusters are encoded in a string to represent the whole clustering. For assigning points to different clusters, these local sub-clusters are considered individually. For the purpose of objective function evaluation, these sub-clusters are merged appropriately to form a variable number of global clusters. Two cluster validity indices, one based on the Euclidean distance, XB-index, and another recently developed point symmetry distance based cluster validity index, Sym-index, are optimized simultaneously to automatically evolve the appropriate number of clusters present in MR brain images. A semi-supervised method is used to select a single solution from the final Pareto optimal front of MCMOClust. The present method is applied on several simulated T1-weighted, T2-weighted and proton density normal and MS lesion magnetic resonance brain images. Superiority of the present method over Fuzzy C-means, Expectation Maximization clustering algorithms and a newly developed symmetry based fuzzy genetic clustering technique (Fuzzy-VGAPS), are demonstrated quantitatively. The automatic segmentation obtained by multiseed based multiobjective clustering technique (MCMOClust) is also compared with the available ground truth information.

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Authors and Affiliations

  1. Image Processing and Modeling, Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Speyerer Strasse 6, 69115, Heidelberg, Germany

    Sriparna Saha

  2. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, 700108, India

    Sanghamitra Bandyopadhyay

Authors
  1. Sriparna Saha
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  2. Sanghamitra Bandyopadhyay
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Correspondence to Sriparna Saha.

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Saha, S., Bandyopadhyay, S. Automatic MR brain image segmentation using a multiseed based multiobjective clustering approach. Appl Intell 35, 411–427 (2011). https://doi.org/10.1007/s10489-010-0231-6

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  • DOI: https://doi.org/10.1007/s10489-010-0231-6

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