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首页> 外文期刊>International journal of fuzzy system applications >Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions: A Comparative Study
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Medical Image Thresholding Using Genetic Algorithm and Fuzzy Membership Functions: A Comparative Study

机译：使用遗传算法和模糊会员功能的医学图像阈值化：比较研究

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摘要

Thresholding is one of the important steps in image analysis process and used extensively in different image processing techniques. Medical image segmentation plays a very important role in surgery planning, identification of tumours, diagnosis of organs, etc. In this article, a novel approach for medical image segmentation is proposed using a hybrid technique of genetic algorithm and fuzzy logic. Fuzzy logic can handle uncertain and imprecise information. Genetic algorithms help in global optimization, gives good results in noisy environments and supports multi-objective optimization. Gaussian, trapezoidal and triangular membership functions are used separately for calculating the entropy and finding the fitness value. CPU time, Root Mean Square Error, sensitivity, specificity, and accuracy are calculated using the three membership functions separately at threshold levels 2, 3, 4, 5, 7 and 9. MRI images are considered for applying the proposed method and the results are analysed. The experimental results obtained prove the effectiveness and efficiency of the proposed method.
机译：阈值是图像分析过程中的重要步骤，并在不同的图像处理技术中广泛使用。医学图像分割在手术规划中发挥着非常重要的作用，鉴定肿瘤，诊断器官等。在本文中，使用遗传算法的混合技术和模糊逻辑提出了一种新的医学图像分割方法。模糊逻辑可以处理不确定和不精确的信息。遗传算法有助于全局优化，在嘈杂的环境中提供良好的结果，并支持多目标优化。高斯，梯形和三角形隶属函数分别用于计算熵并找到健身值。 CPU时间，根均方误差，灵敏度，特异性和准确度，在阈值水平2,3,4,5,7和9. MRI图像中分别计算用于应用所提出的方法，结果是分析。获得的实验结果证明了所提出的方法的有效性和效率。

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