A Robust Brain Image Segmentation Approach Using ABC with FPCM

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In medical field, image processing plays a vital role in research and diagnosing disease. Segmentation of images is widely used for medical purpose such as pre-surgery and post- surgery decisions, which is required for planning treatment. The abnormal growth of tissues can be detected using computer aided detection and it is used for achieving maximum classification accuracy. Magnetic resonance imaging is widely used in computer aided design for the detection of abnormalities. Even though MRI is an efficient method, it is time consuming and needs reasonable amount of human resources. Many studies are going on in the medical field using Markov random fields in segmentation. In this paper, the MRI images are used as a dataset to the proposed algorithm, MRF-artificial bee colony optimization algorithm, with fuzzy possibility c- means is used to obtain the optimal solution. The main aim of the proposed algorithm is to reduce the computational complexity and achieving the higher accuracy. The performance of the proposed algorithm is calculated using region non uniformity, correlation and computation time. The experimental results were compared with the existing approaches such as simulated annealing and MRF with improved genetic algorithm (GA).
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Markov Random Fields; Artificial Bee Colony; Fuzzy Possibility C-Means; Correlation

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