Local energy‐based multimodal medical image fusion in curvelet domain. Issue 6 (19th April 2016)

• Record Type: Journal Article
• Authors: Srivastava, Richa; Prakash, Om; Khare, Ashish
• Is Part Of: IET computer vision. Volume 10:Issue 6(2016)
• Page Start: 513
• Page End: 527
• Publication Date: 2016-04-19
• Subjects: image fusion -- medical image processing -- curvelet transforms -- image resolution -- wavelet transforms; spatial transform based method -- wavelet transform based method -- single pixel-based fusion rule -- anisotropic property -- curvelet transform domain -- spatial resolution imaging -- structural MRI -- single photon emission CT -- positron emission tomography -- magnetic resonance imaging -- computed tomography -- local energy-based multimodal medical image fusion; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2015.0251
• Languages: English
• ISSNs: 1751-9632

Abstract

Abstract : 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.