Brain’s MRI Segmentation for Lesion Detection using Clustering with Grammatical Swarm Based-Adaptable Particle Swarm Optimizer

Authors

  • Tapas Si
  • Arunava De
  • Anup Kumar Bhattacharjee

Keywords:

Magnetic Resonance Imaging, Lesion, Segmentation, Clustering, Grammatical Swarm, Particle Swarm Optimizer

Abstract

Magnetic Resonance Image segmentation is an important image analysis task in medical image processing for diagnosis of diseases. Brain MRI segmentation is done for the proper diagnosis of lesions. In this paper, a new segmentation method using partitional clustering algorithm with Grammatical Swarm Based-Adaptable Particle Swarm Optimizer is proposed for lesion detection of brain MR images. Difficulty in use of segmentation occurs due to presence of noise in the MR images. Therefore, noise is removed using non-local means filter. After segmentation of T2-weighted MR images using the proposed clustering method, lesions are extracted from the MR images. A comparative study has been made with PSO based method using quantitative measurement indices. The experimental results show that proposed method performs better than PSO based method.

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Published

2015-01-01

How to Cite

Tapas Si, Arunava De, & Anup Kumar Bhattacharjee. (2015). Brain’s MRI Segmentation for Lesion Detection using Clustering with Grammatical Swarm Based-Adaptable Particle Swarm Optimizer. Journal of Network and Innovative Computing, 3. Retrieved from https://cspub-jnic.org/index.php/jnic/article/view/97

Issue

Vol. 3 (2015)

Section

Original Article

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