Abstract
In this paper, an optimized seeded region growing (SRG) image segmentation algorithm has been proposed. Here two stage swarm optimization has been used. The algorithm uses cat swarm optimization in first stage for selection of seed points and particle swarm optimization (PSO) in the second stage to determine the similarity criteria and assign the pixels to respective regions to perform image segmentation using seeded region growing. In cat swarm optimization, the information about position, velocity and fitness of each cat is used to select the optimal set of seed points. Given a set of seeds, particle swarm optimization uses the information about position, velocity and fitness of each particle to determine the similarity criteria of pixels and obtains a juxtaposition of image pixels into homogeneous regions. The method for proposal has been compared based on PSNR, RI, VoI, SSIM and TSexec with PSO SRG on various set of benchmark images and its potency is proved by the obtained outcome.
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