Quantitative Analysis and Segmentation of Metastasis Brain Images using Hybrid Mean Shift Clustering

P  Pedda Sadhu Naik   and T  Venu Gopal

doi:10.17485/ijst/2015/v8i35/73803

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Quantitative Analysis and Segmentation of Metastasis Brain Images using Hybrid Mean Shift Clustering

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2015/v8i35/73803

Year: 2015, Volume: 8, Issue: 35, Pages: 1-10

Original Article

Quantitative Analysis and Segmentation of Metastasis Brain Images using Hybrid Mean Shift Clustering

P. Pedda Sadhu Naik^1* and T. Venu Gopal²

¹CSE Department, JNTUK, Kakinada - 533003, Andhra Pradesh, India; [email protected]
²CSE Department, JNT UHCE, Sultanpur - 505501, Telangana, India; [email protected]

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Metastasis brain tumor develops multiple tumors at asymmetrical location of the human brain. MRI imaging is one of the prudent mechanisms to extract the tumor regions and to map the brain for diagnosing. For the better diagnosis, one must detect the tumor accurately and need to calculate the area and volume of the tumor exactly. In this paper, we proposed a novel resolution enhancement technique to improve the quality of MR brain image and Optimized Hybrid Mean Shift Clustering (OHMSC) with region split and merge algorithm to detect the tumor cells from the original MR images and to estimate the tumors from different locations. Simulation results show that the proposed algorithm has performed superior to conventional clustering algorithms such as Fuzzy C-Means (FCM), K-Means and even optimized pillar algorithm.
Keywords: Image Segmentation, Interpolation, Metastasis Brain Tumor, OHMSC Algorithm