An Application of Shearlet Transform for Medical Image Fusion

Recent advances in modern technology have developed the theory for multidimensional data to provide the higher directional sensitivity in medical imaging. Shearlets are a multidirectional and multiscale framework which allows to efficiently encoding anisotropic features in multivariate problem classes. In this paper, we have presented medical image fusion which is the technique of registering and combining complementary information from two or more multimodality images into a single image to improve the imaging quality and reduce randomness and redundancy. Shearlets are the most widely used today due to their optimal sparse approximation properties in medical image analysis improvement to efficiently handle such diverse types and huge amounts of data. Here the medical images can be particular organ focused by the different types of modalities which include X-ray, magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET) and magnetic resonance angiography (MRA) images. Recently, the improvement of medical treatment procedure, medical images fusion being used further in the diagnosing diseases, tumor tissues analysis and treatment plain strategies. Keywords— Shearlet Transform, Medical Images, Image Fusion, Multimodality image, Image registration, CT/MRI images.

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