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

机译:使用新版本的世界杯优化算法对比增强医学图像

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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.
机译:背景:在本文中,提出了一种新方法,用于最佳地增强医学图像的对比度。主要思想是提高伽马校正方法,以提高和突出显示图像信息和基于世界杯优化(WCO)算法的新设计。伽玛校正是一种合适的方法,用于对比增强的效率直接取决于正确选择伽马系数。方法:在本研究中,通过考虑熵,边缘内容和多目标优化来使用新呈现的算法来最佳选择伽马值。结果:将仿真结果与用于呈现方法效率的5个现实方法的仿真结果进行了比较。为此,采用对比度,同质性,加权平均峰值信噪比(WPSNR),增加增强量(EME)和对比度噪声比(CNR)。结论:最终结果表示,所提出的多目标优化算法改善了图像对比度的质量,并且可以提供比其他可比方法更多的细节和信息。

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