Journal ArticleDOI
Anatomical-Functional Image Fusion by Information of Interest in Local Laplacian Filtering Domain
Jiao Du,Weisheng Li,Bin Xiao +2 more
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TLDR
A novel method for performing anatomical magnetic resonance imaging-functional (positron emission tomography or single photon emission computed tomography) image fusion is presented and can obtain better performance, compared with the state-of-the-art fusion methods.Abstract:
A novel method for performing anatomical magnetic resonance imaging-functional (positron emission tomography or single photon emission computed tomography) image fusion is presented. The method merges specific feature information from input image signals of a single or multiple medical imaging modalities into a single fused image, while preserving more information and generating less distortion. The proposed method uses a local Laplacian filtering-based technique realized through a novel multi-scale system architecture. First, the input images are generated in a multi-scale image representation and are processed using local Laplacian filtering. Second, at each scale, the decomposed images are combined to produce fused approximate images using a local energy maximum scheme and produce the fused residual images using an information of interest-based scheme. Finally, a fused image is obtained using a reconstruction process that is analogous to that of conventional Laplacian pyramid transform. Experimental results computed using individual multi-scale analysis-based decomposition schemes or fusion rules clearly demonstrate the superiority of the proposed method through subjective observation as well as objective metrics. Furthermore, the proposed method can obtain better performance, compared with the state-of-the-art fusion methods.read more
Citations
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Book ChapterDOI
Multi-modal Image Fusion Based on Weight Local Features and Novel Sum-Modified-Laplacian in Non-subsampled Shearlet Transform Domain
TL;DR: Wang et al. as discussed by the authors proposed an improved multi-modal medical image fusion method to enhance the visual quality and contrast of the fused image, where the registered source images are firstly decomposed into low-frequency and several high-frequency sub-images via non-subsampled shearlet transform (NSST).
Journal ArticleDOI
Green fluorescent protein and phase contrast image fusion via Spectral TV filter-based decomposition
TL;DR: In this paper , a multi-scale spatial decomposition approach (MSD) is proposed for cross-modal fusion, which is based on spectral total variation (TV) to characterize the spectral signatures into various spatial levels with different filters.
Journal ArticleDOI
FCGP: Infrared and Visible Image Fusion via Joint Contrast and Gradient Preservation
Huibin Yan,Shuoyao Wang +1 more
TL;DR: Wang et al. as mentioned in this paper employed a structure tensor measurement to characterize the similarity between the fused image and the infrared image in terms of thermal radiation information, to better integrate visible appearance details.
Journal ArticleDOI
Medical image fusion based on local Laplacian decomposition and iterative joint filter
TL;DR: Experimental results indicated that the proposed method for integrating the information contained in functional and anatomical medical images has a distinct advantage over existing state‐of‐the‐art algorithms in terms of vision quality and objective metrics.
References
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