Automatic MR brain image segmentation using a multiseed based multiobjective clustering approach |
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Authors: | Sriparna Saha Sanghamitra Bandyopadhyay |
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Affiliation: | (1) Image Processing and Modeling, Interdisciplinary Center for Scientific Computing (IWR), University of Heidelberg, Heidelberg, Germany;(2) Department of Theoretical Bioinformatics, DKFZ (Deutsches Krebsforschungszentrum, German Cancer Research Center), Heidelberg, Germany |
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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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