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

TL;DR

This paper introduces a novel method that uses the Coot Optimization Algorithm to tune parameters in a sub-image histogram equalization technique, improving contrast enhancement for biomedical image segmentation.

Contribution

It applies the Coot Optimization Algorithm to optimize parameters in MVSIHE, enhancing contrast enhancement effectiveness in biomedical images.

Findings

Improved contrast enhancement in biomedical images.

Effective parameter selection using COA.

Enhanced image quality metrics (BRISQUE, NIQE).

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.