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access icon free Local energy-based multimodal medical image fusion in curvelet domain

© The Institution of Engineering and Technology
Received 14/01/2015, Accepted 05/01/2016, Revised 24/12/2015, Published 19/04/2016

Various multimodal medical images like computed tomography (CT), magnetic resonance imaging (MRI), positron emission tomography, single photon emission CT and structural MRI have different characteristics and carry different types of complementary anatomical and functional information. Therefore, fusion of multimodal images is required, in order to achieve good spatial resolution images carrying both anatomical and functional information. In this work, the authors have proposed a fusion technique based on curvelet transform. Curvelet transform is a multiscale, multidirectional transform having anisotropic property and is very efficient in capturing edge points in images. Edges in an image are the important information carrying points used to show better visual structure of the image. They use local energy-based fusion rule which is more effective than single pixel-based fusion rules. Comparison of the proposed method with other existing spatial and wavelet transform based methods, in terms of visual and quantitative measures show the effectiveness of the proposed method. For quantitative analysis of the method, they used five fusion metrics as entropy, standard deviation, edge-strength, sharpness and average gradient.

Inspec keywords: image resolution; curvelet transforms; image fusion; wavelet transforms; medical image processing

Other keywords: computed tomography; spatial transform based method; single pixel-based fusion rule; anisotropic property; local energy-based multimodal medical image fusion; curvelet transform domain; magnetic resonance imaging; positron emission tomography; spatial resolution imaging; structural MRI; wavelet transform based method; single photon emission CT

Subjects: Biomedical measurement and imaging; Integral transforms; Medical and biomedical uses of fields, radiations, and radioactivity; health physics; Patient diagnostic methods and instrumentation; Function theory, analysis; Biology and medical computing; Computer vision and image processing techniques; Optical, image and video signal processing; Integral transforms

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