Using chaos world cup optimization algorithm for medical images contrast enhancement

In this paper, a chaos-based optimization algorithm is employed for medical images contrast enhancement. Here, a weighted combined framework is suggested as the cost function for the contrast enhancement. The method utilizes the advantages of Gamma correction, histogram equalization, and edge information for decreasing the original image features losses. For more enhancements, a piecewise version of Gamma correction is also utilized to decrease the unnatural artifacts in the output image. A combined cost function is employed based on the three aforementioned features and the proposed chaos world cup optimization algorithm is used for maximizing the fitness function. The simulation results have been compared with five state-of-the-art methods for presenting the method efficiency. To do this, contrast, homogeneity, weighted average peak SNR, a measure of enhancement, and contrast noise ratio are employed. The results also applied to two standard medical imaging datasets and compared with the other methods. Final results denoted that the presented multiobjective optimization algorithm improves the quality of the image contrast and can illustrate more details and information toward the other compared methods.