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IJSTR >> Volume 8 - Issue 11, November 2019 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



Performance Analysis Of Brain Tumor Detection Using Optimization Based Fcm Technique On Mri Images

[Full Text]

 

AUTHOR(S)

K. Rajesh Babu, Anishka Singal, Kandukuri Sahiti, Ch. V. S. Sai Jawahar, Syed Shameem

 

KEYWORDS

Brain tumour; Magnetic Resonance Image; Segmentation; K-Means; Fuzzy C Means; Genetic Algorithm; Particle Swarm Optimization

 

ABSTRACT

Early diagnosis of brain tumour may enhance expected lifespan. When not diagnosed at the initial stages, the brain tumour shortens the life expectancy of the infected. Accompanied by several segmentation algorithms, Magnetic Resonance Imaging (MRI) has been typically used as a reliable evaluation protocol. In this paper include some of optimization-based segmentation techniques for brain tumour detection from a Magnetic Resonance (MR) image. This paper provides a comparative study about different optimization-based segmentation techniques. The comparison is done between different parameters that analyze the performance of the segmentation techniques include K-Means and FCM and some of the hybrid techniques for optimized segmentation such as clustering followed by Genetic Algorithm (GA), and clustering with Particle Swarm Optimization (PSO). For these segmentation processes to be done, first pre-process the MRI scan and then apply the further segmentation or optimization techniques to get a clearer and easily detectable tumour. We compare the results for each algorithm and find out the best and efficient method for detecting Brain Tumour from an MRI tumour. As per the performance metrics optimized based segmentation provide a very efficient output in an optimized time as compared with the without optimization of segmentation technique. This paper helps the surgeons to completely detect and diagnose the Brain Tumour without leaving any part of Tumour un-diagnosed.

 

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