Abstract
Image segmentation is one of the inevitable parts of the digital image processing and very useful to solve different real life problems. Biomedical image segmentation is a prime domain of application of digital image processing. and automated computer aided diagnostics process has high dependency on it. Automated identification of different regions of an image are often required by the human experts. Moreover, accurate detection and identification of a region of interest is possible using the automated methods. Errors are common for the human experts and can be reduced and faster results can be achieved with the help of automated and intelligent systems. This work proposes a biomedical image segmentation process using Fractional Order Darwinian Particle Swarm Optimization (FODPSO) and thresholding. The efficiency of the proposed method is tested both visually and quantitatively and the results speaks itself about the efficiency of the proposed work.
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Chakraborty, S., Mali, K., Banerjee, A., Bhattacharjee, M. (2021). A Biomedical Image Segmentation Approach Using Fractional Order Darwinian Particle Swarm Optimization and Thresholding. In: Banerjee, S., Mandal, J.K. (eds) Advances in Smart Communication Technology and Information Processing. Lecture Notes in Networks and Systems, vol 165. Springer, Singapore. https://doi.org/10.1007/978-981-15-9433-5_29
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