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

Authors

Emre Can Kuran1, Umut Kuran2 and Mehmet Bilal Er2, 1Bandırma Onyedi Eylül University, Turkey, 2Harran University, Turkey

Abstract

Contrast enhancement is very important in terms of assessing images in an objective way. Contrast enhancement is also significant for various algorithms including supervised and unsupervised algorithms for accurate classification of samples. Some contrast enhancement algorithms solve this problem by addressing the low contrast issue. Mean and variance based sub-image histogram equalization (MVSIHE) algorithm is one of these contrast enhancements methods proposed in the literature. It has different parameters which need to be tuned in order to achieve optimum results. With this motivation, in this study, we employed one of the most recent optimization algorithms, namely, coot optimization algorithm (COA) for selecting appropriate parameters for the MVSIHE algorithm. Blind/referenceless image spatial quality evaluator (BRISQUE) and natural image quality evaluator (NIQE) metrics are used for evaluating fitness of the coot swarm population. The results show that the proposed method can be used in the field of biomedical image processing.

Keywords

Contrast Enhancement, Coot Optimization Algorithm, Knee X-Ray Images, Biomedical Image Processing.

Full Text  Volume 12, Number 9