A Novel empirical mode decomposition based system for medical image enhancement

S. Bakhtiari, S. Agaian, M. Jamshidi
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引用次数: 2

Abstract

In this paper, we introduce a system for enhancing medical images. The proposed system utilizes Ensemble Empirical Mode Decomposition (EEMD) to decompose the signal into distinct frequency components called intrinsic mode functions (IMFs). These components will be enhanced individually and then recombined to construct the enhanced image. The novelty of the proposed approach is in the method of enhancement and combination of the IMFs. The experimental results demonstrate the performance of the proposed algorithm in visualizing many details that are hidden in the original image. Compared with some existing methods, such as Histogram Equalization, LSD ACE, cascaded unsharp masking and tile-based local enhancement, the new method shows to be more effective in enhancing the images that consist of varying illumination in several regions.
一种新的基于经验模态分解的医学图像增强系统
本文介绍了一种医学图像增强系统。该系统利用集成经验模态分解(EEMD)将信号分解成不同的频率分量,称为固有模态函数(IMFs)。这些组件将被单独增强,然后重新组合以构建增强图像。该方法的新颖之处在于增强和组合imf的方法。实验结果表明,该算法可以有效地将隐藏在原始图像中的许多细节可视化。与直方图均衡化、LSD ACE、级联不锐利掩蔽和基于瓦片的局部增强等方法相比,该方法对多个区域不同照度的图像具有更好的增强效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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