Contrast enhancement of medical images using a new version of the World Cup Optimization algorithm

Yuanping Zhou, Changqin Shi, Bingyan Lai, Giorgos Jimenez


Background: In this paper, a new method for optimal enhancement of the contrast of a medical image is proposed. The main idea is to improve the Gamma correction method to enhance and highlight the image information and the details based on a new design of the World Cup Optimization (WCO) algorithm. Gamma correction is a suitable method for contrast enhancement with an efficiency that directly depends on the correct selection of the Gamma coefficient.
Methods: In this study, a newly presented algorithm was employed for optimal selection of the Gamma value by considering the entropy, edge content, and multi-objective optimization.
Results: The simulation results were compared with 5 state of the art methods for presenting method efficiency. To do this, contrast, homogeneity, weighted average peak signal-to-noise ratio (WPSNR), measure of enhancement (EME), and contrast-to-noise ratio (CNR) were employed.
Conclusions: Final results denote that the presented multi-objective optimization algorithm improves the quality of the image contrast and can provide more details and information than the other comparable methods.