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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Journal ArticleDOI
Medical Image Fusion With Parameter-Adaptive Pulse Coupled Neural Network in Nonsubsampled Shearlet Transform Domain
TL;DR: Experimental results demonstrate that the proposed method can obtain more competitive performance in comparison to nine representative medical image fusion methods, leading to state-of-the-art results on both visual quality and objective assessment.
Journal ArticleDOI
A Phase Congruency and Local Laplacian Energy Based Multi-Modality Medical Image Fusion Method in NSCT Domain
TL;DR: A novel multi-modality medical image fusion method based on phase congruency and local Laplacian energy that achieves competitive performance in both the image quantity and computational costs is presented.
Journal ArticleDOI
Medical Image Fusion via Convolutional Sparsity Based Morphological Component Analysis
TL;DR: Experimental results demonstrate that the proposed CS-MCA model can outperform some benchmarking and state-of-the-art SR-based fusion methods in terms of both visual perception and objective assessment.
Journal ArticleDOI
Unsupervised Deep Image Fusion With Structure Tensor Representations
TL;DR: Deep Image Fusion Network (DIF-Net) as discussed by the authors proposes an unsupervised loss function using the structure tensor representation of the multi-channel image contrasts, which is minimized by a stochastic deep learning solver with large-scale examples.
Journal ArticleDOI
Laplacian Redecomposition for Multimodal Medical Image Fusion
TL;DR: A novel Laplacian redecomposition (LRD) framework tailored to multimodal medical image fusion that outperforms other current popular fusion methods qualitatively and quantitatively.
References
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Journal ArticleDOI
Local Laplacian filters: edge-aware image processing with a Laplacian pyramid
TL;DR: This paper shows state-of-the-art edge-aware processing using standard Laplacian pyramids, and proposes a set of image filters to achieve edge-preserving smoothing, detail enhancement, tone mapping, and inverse tone mapping.
Journal ArticleDOI
Multisource Image Fusion Method Using Support Value Transform
TL;DR: The results demonstrate that the proposed image fusion method using the support value transform is effective and is superior to the conventional image fusion methods in terms of the pertained quantitative fusion evaluation indexes, such as quality of visual information (QAB/F), the mutual information, etc.
Journal ArticleDOI
The multiscale directional bilateral filter and its application to multisensor image fusion
Jianwen Hu,Shutao Li +1 more
TL;DR: The MDBF, which is a multiscale, multidirectional and shift-invariant image decomposition scheme, is used to fuse multisensor images in this paper and demonstrates the superiority of the method compared with conventional methods in terms of visual inspection and objective measures.
Journal ArticleDOI
A Neuro-Fuzzy Approach for Medical Image Fusion
Sudeb Das,Malay K. Kundu +1 more
TL;DR: This paper addresses a novel approach to the multimodal medical image fusion (MIF) problem, employing multiscale geometric analysis of the nonsubsampled contourlet transform and fuzzy-adaptive reduced pulse-coupled neural network (RPCNN).
Journal ArticleDOI
Medical image fusion using multi-level local extrema
TL;DR: This paper proposes a new fusion algorithm for multi-modal medical images based on MLE that enables the decomposition of input images into coarse and detailed layers in the MLE schema, and utilizes local energy and contrast fusion rules for coefficient selection in the different layers.
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