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Contrast enhancement is very important in terms of assessing images in anobjective way. Contrast enhancement is also significant for various algorithmsincluding supervised and unsupervised algorithms for accurate classification ofsamples. Some contrast enhancement algorithms solve this problem by addressingthe low contrast issue. Mean and variance based sub-image histogramequalization (MVSIHE) algorithm is one of these contrast enhancements methodsproposed in the literature. It has different parameters which need to be tunedin order to achieve optimum results. With this motivation, in this study, weemployed one of the most recent optimization algorithms, namely, cootoptimization algorithm (COA) for selecting appropriate parameters for theMVSIHE algorithm. Blind/referenceless image spatial quality evaluator (BRISQUE)and natural image quality evaluator (NIQE) metrics are used for evaluatingfitness of the coot swarm population. The results show that the proposed methodcan be used in the field of biomedical image processing.

Sub-Image Histogram Equalization using Coot Optimization Algorithm for Segmentation and Parameter Selection