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Segmentation Of Brain MRI Images For Tumor Detection By Optimizing C-Means Clustering Method

kailash sinha*, G. R. Sinha**

* Research Scholar, Department of Electronics and Telecommunication Engineering, Shri Shankaracharya Group of Institutions, Bhilai, India.

** Professor and Associate Director, Faculty of Engineering and Technology, Shri Shankaracharya Group of Institutions, Bhilai, India.

Periodicity:May - July'2013
DOI : https://doi.org/10.26634/jic.1.3.2352

Abstract

Magnetic Resonance Imaging (MRI) is one of the best technologies currently being used for diagnosing brain tumor. Tumor detection and segmentation from MRI image is very important in medical imaging but apart from time taken in diagnosis accuracy of detection is also poor. For segmentation of medical images, clustering techniques such as kmeans and c-means clustering methods are widely used. The authors have implemented c-means clustering method and optimized its performance by using genetics algorithm. The combined approach resulted in improvement of segmentation efficiency and higher value of true positive pixels belonging to tumor region.

Keywords

MRI, Brain Tumor, Segmentation, C-Means Clustering, Genetic Algorithm.

How to Cite this Article?

Sinha, k., and Sinha, G.R. (2013). Methods of Placement and Installation of UFM To Extend The Linearity Range of Measurement. i-manager’s Journal on Instrumentation and Control Engineering, 1(3), 14-21. https://doi.org/10.26634/jic.1.3.2352

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