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Analysis and Comparison of Different Wavelet Transform Methods Using Benchmarks for Image Fusion

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Document pages: 8 pages

Abstract: In recent years, many research achievements are made in the medical imagefusion field. Medical Image fusion means that several of various modality imageinformation is comprehended together to form one image to express itsinformation. The aim of image fusion is to integrate complementary andredundant information. CT MRI is one of the most common medical image fusion.These medical modalities give information about different diseases.Complementary information is offered by CT and MRI. CT provides the bestinformation about denser tissue and MRI offers better information on softtissue. There are two approaches to image fusion, namely Spatial Fusion andTransform fusion. Transform fusion uses transform for representing the sourceimages at multi-scale. This paper presents a Wavelet Transform image fusionmethodology based on the intensity magnitudes of the wavelet coefficients andcompares five variations of the wavelet transform implemented separately inthis fusion model. The image fusion model, using the Discrete Wavelet Transform(DWT), the Stationary Wavelet Transform (SWT), the Integer Lifting WaveletTransform (ILFT) the dual-tree Complex Wavelet Transform (DT CWT) and dual-treeQ-shift dual-tree CWT, is applied to multi-modal images. The resulting fusedimages are compared visually and through benchmarks such as Entropy (E), PeakSignal to Noise Ratio, (PSNR), Root Mean Square Error (RMSE), Image QualityIndex (IQI) and Standard deviation (SD) computations.

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