An Optimal Approach for Medical Image Analysis
- 1 Affiliated to Bharathiyar University, India
Problem statement: Image segmentation is the process that subdivides an image into its constituent parts and extracts the objects. It is one of the most critical tasks in automatic image analysis because the subdivided results will affect all the subsequent processes of image analysis, such as object representation and description, feature measurement and even the following higher level tasks such as object classification and scene interpretation by optimized results. Approach: In this study, we proposed an optimal approach for medical image segmentation based on the combination of Particle Swarm Optimization (PSO) and Global Minimization by Active Contour (GMAC) methods. PSO is a population based new evolutionary algorithm in the field of image segmentation where the image homogeneous part can be detected. The grouped part from PSO is again treated with GMAC to reduce the complex region of image parts. Results: The feasibility of these algorithms for analyzing is presented through experimental investigation. The simulation results give that the proposed optimal approach gives efficient results for image segmentation. Conclusion/Recommendation: The performance of the proposed study is compared with the existing traditional algorithm and real time medical diagnosis image.
Copyright: © 2021 J. Umamaheswari and G. Radhamani. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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- Particle Swarm Optimization (PSO)
- Global Minimization by Active Contour (GMAC)
- Parametric Active Contour (PAC)
- Swarm Intelligence (SI)
- Geometric Active Contour (GAC)
- existing traditional algorithm