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A New Composite Multimodality Image Fusion Method Based on Shearlet Transform and Retina Inspired Model


Scalable and Secure Big Data I

A Wavelet-Predominant Algorith

Cross-Modality Image Synthesi
Abstract


Medical imaging is a very important element in disease diagnosis. MRI image has structural information, while PET image has functional information. However, there is no medical imagery device that has both structural and functional information simultaneously. Thus, the image fusion technique is used. This work concentrates on PET and MRI fusion. It is based on the combination of retina-inspired model and Non-Subsampled shearlet transform. In the first step, the high-frequency component is obtained by applying the shearlet transform to the MRI image, which produces sub-images in several scales and directions, and by adding up these images together a single edge image is reconstructed. In the second step, the PET image is transferred from RGB color space into IHS color space. Then the low-frequency component is produced by applying a Gaussian low pass filter to the luminance channel of the PET image. By adding up low frequency component and high-frequency component together and transferring the result from IHS color space to RGB color space the fused image is obtained.

KeyWords
Image fusion, MRI, Non-subsampled Shearlet transform, PET, Retina Inspired Model, Spatial and Spectral transforms.



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