Multi-modality medical image fusion based on image decomposition framework and nonsubsampled shearlet transform

Liu, Xingbin; Mei, Wenbo; Du, Heng

doi:10.1016/j.bspc.2017.10.001

Cited by 85 publications

(39 citation statements)

References 28 publications

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“…For example, Chen et al (4) developed moving frames for keeping the local geometry in image processing. Liu et al (5) presented a novel approach called non-subsampled shearlet transform (NSST) method, where more edges and text data can be shifted from the primary images into the final fused images. In recent years, methods on the hybridized fuzzy Contourlet were employed to the fusion of the multi-level clinical images, where the high frequency of the contourlet domain and approximation properties have been fused applying according to the fuzzy techniques (6).…”

Section: Introductionmentioning

confidence: 99%

Application of hybrid particle swarm and ant colony optimization algorithms to obtain the optimum homomorphic wavelet image fusion

Jiang

2020

Ann Transl Med

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Background: There are various applications for medical image fusion schemes in different medical clinics.Here, the generalized version of the homomorphic filtering technique involving the Fourier domain for image and signal processing is a proper method. Methods:The methods on the wavelet transform proposes some advantages in the discretization of multimodality medical images fusion, conducted in the Fourier spectrum. In the present study, an optimal version of the homomorphic fusion, namely optimum homomorphic wavelet fusion (OHWF) on the hybrid particle swarm and ant colony optimization methods, is presented. The presented OHWF, including some domains including wavelet and logarithmic, and besides, the wavelet allows the OHWF technique to decompose the images in the multi-level process.Results: In this work, the modality one approximation coefficients and the coefficients belong to modality two are presented in adder1. While in the case of adder two, the modality one optimal scaled detailed constant values of modality one and the approximation coefficients refer to modality two are added in conjunction. The pixel-based averaged principle is applied to fuse the address one and two results simultaneously. First, the intended fusion technique is authenticated, applying different fusion assessment metrics for MR-PET, MR-SPECT, MR T1-T2, and MR-CT image fusions. And then, the proposed hybrid particle swarm optimizer (PSO) and ACO algorithms applied to obtain the best image fusion. Conclusions:The empirical data illustrates that the presented method performs a desiring ability in image fusion in the case of functional and structural data.

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Section: Introductionmentioning

confidence: 99%

Application of hybrid particle swarm and ant colony optimization algorithms to obtain the optimum homomorphic wavelet image fusion

Jiang

2020

Ann Transl Med

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“…Finally, the fused image is reconstructed through the corresponding inverse transform. The MST methods mainly contain the Laplacian pyramid (LP) [6], the wavelet transform (WT) [27,34], the non-subsampled contourlet transform (NSCT) [49], and the non-subsampled shearlet transform (NSST) [4,23,38]. However, if the MST method performs without other fusion measures, some unexpected block effect may appear [39].…”

Section: Current Challenges In Multimodal Image Fusionmentioning

confidence: 99%

Multimodal medical image fusion algorithm in the era of big data

Tan

Tiwari

Pandey

et al. 2020

Neural Comput & Applic

122

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In image-based medical decision-making, different modalities of medical images of a given organ of a patient are captured. Each of these images will represent a modality that will render the examined organ differently, leading to different observations of a given phenomenon (such as stroke). The accurate analysis of each of these modalities promotes the detection of more appropriate medical decisions. Multimodal medical imaging is a research field that consists in the development of robust algorithms that can enable the fusion of image information acquired by different sets of modalities. In this paper, a novel multimodal medical image fusion algorithm is proposed for a wide range of medical diagnostic problems. It is based on the application of a boundary measured pulse-coupled neural network fusion strategy and an energy attribute fusion strategy in a non-subsampled shearlet transform domain. Our algorithm was validated in dataset with modalities of several diseases, namely glioma, Alzheimer's, and metastatic bronchogenic carcinoma, which contain more than 100 image pairs. Qualitative and quantitative evaluation verifies that the proposed algorithm outperforms most of the current algorithms, providing important ideas for medical diagnosis.

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“…To evaluate the fusion performance, the fusion result of the proposed method is compared with seven state-of-the-art fusion methods which are TGV, 23 GTF, 21 MGGFF, 18 GFF, 17 PAPCNN, 16 MFDF-NSST, 14 and LP-SR 4…”

Section: A | Visual Observationmentioning

confidence: 99%

An efficient fusion algorithm combining feature extraction and variational optimization for CT and MR images

Wang

Yang

2020

J Applied Clin Med Phys

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In medical image processing, image fusion is the process of combining complementary information from different or multimodality images to obtain an informative fused image in order to improve clinical diagnostic accuracy. In this paper, we propose a two-stage fusion framework for computed tomography (CT) and magnetic resonance (MR) images. First, the intensity and geometric structure features in both CT and MR images are extracted by the saliency detection method and structure tensor, respectively, and an initial fused image is obtained. Then, the initial fused image is optimized by a variational model which contains a fidelity term and a regularization term. The fidelity term is to retain the intensity of the initial fused image, and the regularization term is to constrain the gradient information of the fused image to approximate the MR image. The primal-dual algorithm is proposed to solve the variational problem. The proposed method is applied on five pairs of clinical medical CT and MR-T1\MR-T2 images, and the comparison metrics SF, MI, Q AB=F , Q W , and VIFF are calculated for assessment. Compared with seven state-of-the-art methods, the proposed method shows a comprehensive advantage in preserving the salient intensity features, as well as texture structure information, not only in visual effects but also in objective assessments.

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Multi-modality medical image fusion based on image decomposition framework and nonsubsampled shearlet transform

Cited by 85 publications

References 28 publications

Application of hybrid particle swarm and ant colony optimization algorithms to obtain the optimum homomorphic wavelet image fusion

Application of hybrid particle swarm and ant colony optimization algorithms to obtain the optimum homomorphic wavelet image fusion

Multimodal medical image fusion algorithm in the era of big data

An efficient fusion algorithm combining feature extraction and variational optimization for CT and MR images

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