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Contrast enhancement of MRI images using morphological transforms and PSO

机译：使用形态转化和PSO的MRI图像对比增强

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摘要

Medical imaging plays a crucial role in correct extraction of the significant information for monitoring the patient's health and providing the quality treatment. A deluge of medical images requires initial interpretation for the presence of any abnormality, however, the correct diagnosis requires the images to be of good quality. To cope with the problem of poor contrast in medical images, this paper presents a method based on morphological transforms to improve the quality of the images. The proposed method incorporates Particle Swarm Optimization to find an optimum value of a parameter which controls the enhancement of the resulting image. The proposed algorithm is executed on a set of MRI images for testing its efficacy. The experimental results are compared in terms of both qualitative and quantitative parameters. The mean opinion score is obtained with the help of experts, which clearly shows the better performance of the proposed method. Furthermore, the parameters like Contrast Improvement Ratio, signal-to-noise ratio, peak signal-to-noise ratio, PL, and Structural Similarity Index are evident of better performance of proposed method when compared with the state-of-the-art methods and few recent methods. The comparison shows that the performance of the proposed method based on morphological transforms incorporating Particle Swarm Optimization is better not only visually but also in terms of other evaluation parameters.
机译：医学成像在正确提取监测患者健康和提供优质治疗的重要信息中起着至关重要的作用。苏打医学图像需要对存在任何异常的存在初始解释，然而，正确的诊断需要图像质量很好。为了应对医学图像中对比度差的问题，本文介绍了一种基于形态变换的方法，提高了图像质量。所提出的方法包括粒子群优化，以找到控制所得图像增强的参数的最佳值。所提出的算法在一组MRI图像上执行以测试其功效。在定性和定量参数方面比较实验结果。在专家的帮助下获得平均意见分数，该评分清楚地表明了所提出的方法的性能更好。此外，与最先进的方法相比，相似性提高比率，信噪比，峰值信噪比，PL和结构相似度的参数是明显的提出的方法最近的一些方法。比较表明，基于包含粒子群优化的形态转化的所提出的方法的性能不仅在视觉上更好，而且还就其他评估参数而言。

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