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Automatic MRI Brain Image Segmentation Using Gravitational Search-Based Clustering Technique

Automatic MRI Brain Image Segmentation Using Gravitational Search-Based Clustering Technique

Vijay Kumar, Jitender Kumar Chhabra, Dinesh Kumar
Copyright: © 2014 |Pages: 14
ISBN13: 9781466645585|ISBN10: 146664558X|EISBN13: 9781466645592
DOI: 10.4018/978-1-4666-4558-5.ch015
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MLA

Kumar, Vijay, et al. "Automatic MRI Brain Image Segmentation Using Gravitational Search-Based Clustering Technique." Research Developments in Computer Vision and Image Processing: Methodologies and Applications, edited by Rajeev Srivastava, et al., IGI Global, 2014, pp. 313-326. https://doi.org/10.4018/978-1-4666-4558-5.ch015

APA

Kumar, V., Chhabra, J. K., & Kumar, D. (2014). Automatic MRI Brain Image Segmentation Using Gravitational Search-Based Clustering Technique. In R. Srivastava, S. Singh, & K. Shukla (Eds.), Research Developments in Computer Vision and Image Processing: Methodologies and Applications (pp. 313-326). IGI Global. https://doi.org/10.4018/978-1-4666-4558-5.ch015

Chicago

Kumar, Vijay, Jitender Kumar Chhabra, and Dinesh Kumar. "Automatic MRI Brain Image Segmentation Using Gravitational Search-Based Clustering Technique." In Research Developments in Computer Vision and Image Processing: Methodologies and Applications, edited by Rajeev Srivastava, S. K. Singh, and K. K. Shukla, 313-326. Hershey, PA: IGI Global, 2014. https://doi.org/10.4018/978-1-4666-4558-5.ch015

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Abstract

Image segmentation plays an important role in medical imaging applications. In this chapter, an automatic MRI brain image segmentation framework using gravitational search based clustering technique has been proposed. This framework consists of two stage segmentation procedure. First, non-brain tissues are removed from the brain tissues using modified skull-stripping algorithm. Thereafter, the automatic gravitational search based clustering technique is used to extract the brain tissues from the skull stripped image. The proposed algorithm has been applied on four simulated T1-weighted MRI brain images. Experimental results reveal that proposed algorithm outperforms the existing techniques in terms of the structure similarity measure.

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