Multi-modal brain image fusion based on multi-level edge-preserving filtering

Tan, Wei; Thitøn, William; Xiang, Pei; Zhou, Huixin

doi:10.1016/j.bspc.2020.102280

Cited by 76 publications

(21 citation statements)

References 32 publications

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“…As a result of these physiology-inspired neural networks’ outstanding ability to extract dynamic information inside multi-dimensional signals, they have been widely used in numerous fields. Instances include feature extraction [ 27 ], pulse shape discrimination [ 28 , 29 , 30 ], image encryption [ 31 ], and image segmentation and fusion [ 32 , 33 ].…”

Section: Methodsmentioning

confidence: 99%

Multimodal Image Fusion for X-ray Grating Interferometry

Liu

Jiang

et al. 2023

Sensors

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X-ray grating interferometry (XGI) can provide multiple image modalities. It does so by utilizing three different contrast mechanisms—attenuation, refraction (differential phase-shift), and scattering (dark-field)—in a single dataset. Combining all three imaging modalities could create new opportunities for the characterization of material structure features that conventional attenuation-based methods are unable probe. In this study, we proposed an image fusion scheme based on the non-subsampled contourlet transform and spiking cortical model (NSCT-SCM) to combine the tri-contrast images retrieved from XGI. It incorporated three main steps: (i) image denoising based on Wiener filtering, (ii) the NSCT-SCM tri-contrast fusion algorithm, and (iii) image enhancement using contrast-limited adaptive histogram equalization, adaptive sharpening, and gamma correction. The tri-contrast images of the frog toes were used to validate the proposed approach. Moreover, the proposed method was compared with three other image fusion methods by several figures of merit. The experimental evaluation results highlighted the efficiency and robustness of the proposed scheme, with less noise, higher contrast, more information, and better details.

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Section: Methodsmentioning

confidence: 99%

Multimodal Image Fusion for X-ray Grating Interferometry

Liu

Jiang

et al. 2023

Sensors

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“…Hongpeng proposes it for different image modalities such as fusing infrared and visible images and fusing medical images. In 2018, Xin Jin et al suggested a multi-focus image fusion on Gray images and colour images with two fusion rules (average fusion rule and maximum fusion rule) using LSF, PCNN, LPT [17]. The complexity of this IF method is acceptable with better performance.…”

Section: Literature Surveymentioning

confidence: 99%

A Review on Spatial and Transform Domain Image Fusion Methods

Satyanarayana

Mohanaiah²

2023

Advances in Transdisciplinary Engineering

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Image fusion aims to collect better information from an image obtained by combining two or more different images. Image fusion has many applications such as remote sensing, medical imaging, medical diagnosis, robotics, surveillance, and image enhancements. This article presents the strengths and weakness of recent arrivals in the literature on spatial and transform domain. A comparative analysis among recently existed literature of various types of image fusing methodologies. A generalized flow diagram for image fusion which is universally acceptable to all types of images such as medical images, visible images, infrared images, multi-focus images, multi-exposure images, hyperspectral image and multispectral images is depicted. Various effective qualitative and quantitative performance measures useful for image fusion with reference image and without reference image are also tabled. At last, recommendations are given as per current research requirements for better performance with decent complexity.

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“…In the field of image processing, edge-preserving filters have recently become an active topic of research. 37 Weighted least squares, bilateral filters, 38 and guided filters 39 can avoid edge oscillation effects. Because the image is filtered using an edge-preserving filter, it can retain the large edge structure of the image, while filtering out the smaller undulations in the image, which mainly include image texture and noise.…”

Section: Iterative Joint Filtermentioning

confidence: 99%

“…In the field of image processing, edge‐preserving filters have recently become an active topic of research 37 . Weighted least squares, bilateral filters, 38 and guided filters 39 can avoid edge oscillation effects.…”

Section: Relevant Workmentioning

confidence: 99%

Medical image fusion based on local Laplacian decomposition and iterative joint filter

Chao

Wang

et al. 2022

Int J Imaging Syst Tech

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Previous multi‐modal medical image fusion methods have suffered from color distortion, blurring, and noise. To address these problems, we propose a method for integrating the information contained in functional and anatomical medical images. In the proposed method, multi‐scale image representation of input images is produced by local Laplacian filtering. The rgb2ycbcr algorithm and iterative joint filters are then used to produce fused approximate images. The residual images are divided into regions of interest and noninterest regions, and then a local energy maximization scheme and local energy average scheme are used to combine these regions. Fused interest areas and fused noninterest areas are combined to produce fused residual images. Finally, an inverse local Laplacian filter is used as a reconstruction tool to produce a fused image. Experimental results indicated that our method has a distinct advantage over existing state‐of‐the‐art algorithms in terms of vision quality and objective metrics.

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Multi-modal brain image fusion based on multi-level edge-preserving filtering

Cited by 76 publications

References 32 publications

Multimodal Image Fusion for X-ray Grating Interferometry

Multimodal Image Fusion for X-ray Grating Interferometry

A Review on Spatial and Transform Domain Image Fusion Methods

Medical image fusion based on local Laplacian decomposition and iterative joint filter

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