NDLI: Hybrid Medical Image Fusion method Using Wavelet and Fast Discrete Curvelet Transform

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Hybrid Medical Image Fusion method Using Wavelet and Fast Discrete Curvelet Transform

Content Provider	Semantic Scholar
Author	Dutt, Gordon R. Bindu, Ch. Hima Prasad, R. Satya
Abstract	Transform) is initially applied to obtain fine and coarse details of an image. To obtain curved edges information of the same image, the DWT coefficients are again transformed by using FDCT (Fast Discrete Curvelet Transform). For fusing we have chosen two different criterions for two different bands. In case of Low frequency band the local variance value component is chosen for fusing, whereas in case of high frequency bands maximum value method is chosen as a feature for fusing the two images. Finally the proposed algorithm was applied to various medical images and the obtained fused image gives more information than either MRI or CT scan images. I. INTRODUCTION In computer vision, Multisensor Image fusion is the process of combining relevant information from two or more images into a single image. The resulting image will be more informative than either of the input images. Image fusion has become a common term used within medical diagnostics and treatment. Fused images may be created from multiple images from the same imaging modality by combining information from multiple modalities, such as magnetic resonance image (MRI), computed tomography (CT), positron emission tomography (PET), and single photon emission computed tomography (SPECT).In radiology and radiation oncology, these images serve different purposes. For example, CT images are used more often to ascertain differences in tissue density while MRI images are typically used to diagnose brain tumours.
File Format	PDF HTM / HTML
Alternate Webpage(s)	https://www.researchgate.net/profile/Hima_Bindu31/publication/266717237_Hybrid_Medical_Image_Fusion_method_Using_Wavelet_and_Fast_Discrete_Curvelet_Transform/links/5437f3940cf2590375c56bcf.pdf
Language	English
Access Restriction	Open
Content Type	Text
Resource Type	Article

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