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Multimodal Medical Image Fusion Under Redundant Transforms

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Multimodal medical image fusion gives the complementary information present in each imaging sensor combined into a single image. This is done to reduce the data, better visualization and clinical diagnosis. In this paper, an efficient fusion approach based on redundant transforms, which, in particular, restores and shift-invariant the image as fused image contains artifacts when reconstructed. Different wavelets like Daubechies, orthogonal, Biorthogonal, Symlets and Curvelets are compared with the proposed technique. Medical image fusion results are determined using subjective and objective analysis with the existing state of the art techniques which include wavelets and curvelets. The comparative analysis of the obtained fusion results are performed with RMS error, Entropy, Correlation coefficient, PSNR, Standard deviation, Spatial Frequency and Median frequency. The combined subjective and objective evaluation of the proposed fusion method shows the accuracy and increased robustness.
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Medical Image Fusion; Redundant Transforms; Wavelets; Curvelets

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