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Associate Professor, Faculty of Engineering and Technology, Annamalai University , Annamalai Nagar, Tamil Nadu , India
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Associate Professor, Department of Electrical and Electronics Engineering, New Horizon College of Engineering , Bengaluru , India
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Professor, Department of Electrical and Electronics Engineering, Kangeyam Institute of Technology , Tirupur , India
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Associate Professor, Department of Electrical and Electronics Engineering, Kangeyam Institute of Technology , Tirupur , India
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Associate Professor, Department of Electrical and Electronics Engineering, St. Joseph’s Institute of Technology , Chennai , India
Associate Professor, Department of Electrical and Electronics Engineering, St.Joseph's College of Engineering , Chennai , India
Image segmentation plays an important role in medical diagnosis and recognition, but the traditional methods of multilevel thresholding have exponential computation complexity with the number of thresholds. The study corresponds to the necessity to have a computationally effective parameter-free optimization to support fast clinical decision-making. The study suggests two optimization systems that are used to optimize image segmentation, namely the Jaya algorithm and Stochastic Fractal Search (SFS). Jaya algorithm, with its single-phase update mechanism and no algorithm-specific parameters, is used to calculate optimal thresholds based on the maximization of Entropy in Kapur. At the same time, the SFS algorithm is based on the idea of natural fractal patterns and the diffusion of particles, which are used to maximize the between-class variance of Otsu. These two techniques were strictly tested on 256 × 256 8-bit benchmark images (Cameraman, Lena, and Peppers). The results of numerical assessments indicate that the two algorithms are able to approach optimal threshold values irrespective of varying levels (K = 2, 3, 4, 5). In the Jaya algorithm, especially, the computational efficiency was much better, and the minimum processing time was used without compromising the quality of segmentation. When compared to the existing metaheuristics such as GA and PSO, it is shown that the suggested methods are more stable and robust and do not change in levels even when the number of thresholds grows. The results place the Jaya and SFS algorithms as promising algorithms to perform multi-level thresholding of images. They are very appropriate in real-time medical imaging and other technical applications that demand high-quality segments with minimal computational overhead.
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