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首页> 外文期刊>International Journal of Engineering Research and Applications >Performance Comparison Of Medical Image Fusion Methods Based On Redundant Discrete Wavelet Transform, Wavelet Packet Transform And Contourlet Transform
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Performance Comparison Of Medical Image Fusion Methods Based On Redundant Discrete Wavelet Transform, Wavelet Packet Transform And Contourlet Transform

机译：基于冗余离散小波变换，小波包变换和轮廓波变换的医学图像融合方法性能比较

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摘要

Image fusion is the process of combining relevant information from two or more images into a single fused image. The resulting image will be more informative than any of the input images. The fusion in medical images is necessary for efficient diseases diagnosis from multimodality, multidimensional and multi parameter type of images. This paper describes a multimodality medical image fusion system using different fusion techniques and the resultant is analysed with quantitative measures. Initially, the registered images from two different modalities such as CT (anatomical information) and MRI - T2, FLAIR (pathological information) are considered as input, since the diagnosis requires anatomical and pathological information. Then the fusion techniques based on Redundancy Discrete Wavelet Transform (RDWT), Wavelet Packet Transform and Contourlet Transform are applied. Further the fused image is analyzed with quantitative metrics such as Standard Deviation (SD), Entropy (EN), and Signal to Noise Ratio (SNR) for performance evaluation. From the experimental results it is observed that RDWT method provides better information quality for SD and SNR metric and the Contourlet Transform method provides better information quality using EN metric
机译：图像融合是将来自两个或更多图像的相关信息组合为一个融合图像的过程。生成的图像将比任何输入图像更具信息性。医学图像中的融合对于从多模态，多维和多参数类型的图像进行有效的疾病诊断是必要的。本文描述了一种使用不同融合技术的多模态医学图像融合系统，并对结果进行了定量分析。最初，由于诊断需要解剖学和病理学信息，因此将来自两种不同形式（例如CT（解剖学信息）和MRI-T2，FLAIR（病理学信息））的注册图像视为输入。然后应用了基于冗余离散小波变换（RDWT），小波包变换和轮廓波变换的融合技术。进一步地，用诸如标准偏差（SD），熵（EN）和信噪比（SNR）之类的定量度量来分析融合图像，以进行性能评估。从实验结果可以看出，RDWT方法为SD和SNR度量提供了更好的信息质量，而Contourlet变换方法使用EN度量提供了更好的信息质量。

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