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MRI Brain Image Segmentation Using Interactive Multiobjective Evolutionary Approach

MRI Brain Image Segmentation Using Interactive Multiobjective Evolutionary Approach

Anirban Mukhopadhyay
Copyright: © 2016 |Pages: 20
ISBN13: 9781522500582|ISBN10: 1522500588|EISBN13: 9781522500599
DOI: 10.4018/978-1-5225-0058-2.ch002
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MLA

Mukhopadhyay, Anirban. "MRI Brain Image Segmentation Using Interactive Multiobjective Evolutionary Approach." Handbook of Research on Natural Computing for Optimization Problems, edited by Jyotsna Kumar Mandal, et al., IGI Global, 2016, pp. 10-29. https://doi.org/10.4018/978-1-5225-0058-2.ch002

APA

Mukhopadhyay, A. (2016). MRI Brain Image Segmentation Using Interactive Multiobjective Evolutionary Approach. In J. Mandal, S. Mukhopadhyay, & T. Pal (Eds.), Handbook of Research on Natural Computing for Optimization Problems (pp. 10-29). IGI Global. https://doi.org/10.4018/978-1-5225-0058-2.ch002

Chicago

Mukhopadhyay, Anirban. "MRI Brain Image Segmentation Using Interactive Multiobjective Evolutionary Approach." In Handbook of Research on Natural Computing for Optimization Problems, edited by Jyotsna Kumar Mandal, Somnath Mukhopadhyay, and Tandra Pal, 10-29. Hershey, PA: IGI Global, 2016. https://doi.org/10.4018/978-1-5225-0058-2.ch002

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Abstract

The problem of image segmentation is frequently modeled as a problem of clustering the pixels of the images based on their intensity levels. In some recent studies, multiobjective clustering algorithms, where multiple cluster validity measures are optimized simultaneously for yielding robust clustering solutions have been proposed. It has been observed that the same set of validity measures optimized simultaneously do not generally perform well for all image datasets. In view of this, in this article, an interactive approach for multiobjective clustering is proposed for segmentation of multispectral Magnetic Resonance Image (MRI) of the human brain. In this approach, a human decision maker interacts with the multiobjective evolutionary clustering technique during execution in order to obtain the final clustering, the suitable set of validity measures for the input image, as well as the number of clusters by employing a variable-length encoding of the chromosomes. The effectiveness of the proposed method is demonstrated on many simulated normal and MS lesion MRI brain images.

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