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Color image segmentation is the primary factor to provide the intended information from the input image. The straightforward method called multilevel thresholding (MLT) is used to analyse the various classes of complex images. But, when the level of threshold increases, computational difficulty increases. Hence, MLT with most promising objective functions such as Kapur, Otsu and minimum cross entropy aided with cuckoo search algorithm (CSA) is used. The efficient metaheuristic cuckoo search algorithm’s controlling parameter balances the local and global search. In this paper, the efficacy of CSA at 4,5,6 and 7 threshold levels with various fitness functions are utilized for precise image segmentation. It is seen from experimental results, the Otsu based cuckoo search algorithm outperform than Kapur and MCE. Quality metrices such as computational time, PSNR (peak signal to noise ratio) and SSIM (structural similarity index) authenticate the exploration and exploitation capability of CSA algorithm for real-world applications.

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Medical image segmentation is the basic pre-processing step to infer information from the input image with RGB color space. In this paper, multilevel thresholding (MLT) with most optimistic objective functions such as Kapur and Otsu are used for image segmentation. But the MLT suffers from high execution time with the increase in number of threshold levels while exploring for optimal threshold. This difficulty is eased by the robust teachinglearning based optimization (TLBO) algorithm. It mimics the classroom environment where the student gains knowledge from the teacher. The main aspect of the TLBO is the use of less algorithm specific parameters in search process and it avoids premature convergence and getting trapped with sub-optimal solutions. The performance of TLBO algorithm is compared with cuckoo search (CS) algorithm at 4, 5, 6 and 7 threshold levels. Experimental results confirm that the Otsu based MLT outperform the Kapur objective function. Exploration and exploitation reveal the fast convergence and are confirmed by metrics such as computational time, peak signal to noise ratio (PSNR) and structural similarity index (SSIM). This affirms the inclusion of TLBO algorithm for precise medical image segmentation.

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