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IJSTR >> Volume 9 - Issue 5, May 2020 Edition

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

Website: http://www.ijstr.org

ISSN 2277-8616

Brain Tumor Analysis Using Various Evolutionary Segmentation Techniques

[Full Text]



K. Subhashini, K. Kowsalya



Adaptive median, K-mean, PSO, Fuzzy C-mean, MRI image.



Recently, the medical image diagnosis is important field in this current situation. We have different methods to diagnosis an image such as Computed Tomography (CT), Positron Emission Tomography (PET), Magnetic resonance imaging (MRI) etc. these methods are allowed to find the smallest disease in the human body. An abnormal growth of the cell to distress a proper brain functioning is considered as brain tumor. The main aim of this research to detect an image information with minimum error possible. The MRI scan is to get the image information and to detect the cancerous tissues accurately because its better quality and high resolution of image compared with other technologies. Different type of techniques is implemented and executed to detect a cancerous and non-cancerous image. In this process of identifying a cancerous image can be categorized into different level; pre-processing, segmentation, feature extraction and classification. In this present study, using four optimized algorithms like Particle Swarm Optimization, Fuzzy c-mean clustering and hybrid particle swarm optimization-Fuzzy C-mean (PSO-FCM), to extract the tissues from the brain has been analyzed and implemented. In Pre-processing, mean, median and adaptive median was compared and proved that the proposed adaptive median filter are gives better accuracy rate to denoising image. A sample MRI images of brain using MATLAB and proved the hybrid PSO-FCM gives highest accuracy rate of 95.79%.



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