Fusion of 3-D medical image gradient domain based on detail-driven and directional structure tensor

Wang, Yuanjun

doi:10.3233/xst-200684

Cited by 6 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Guided image filtering (GIF) is a representative local algorithm in recent years, and its calculation method is also simple [ 18 ]. However, due to its fixed regularization coefficient, the image will produce halo, including weighted guided image filtering (WGIF) and gradient domain guided image filtering (GGIF) [ 19 , 20 ]. Therefore, it is proposed to improve the GIF algorithm and add an edge-aware weighting factor to the regularization coefficient to solve the above problems [ 21 ], and good results are obtained.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Algorithm-Based Differential Diagnosis of Crohn’s Disease and Ulcerative Colitis by CT Image

Jiang

Kuang

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

The aim of this study was to investigate the effect of low-dose CT enterography (CTE) based on modified guided image filtering (GIF) algorithm in the differential diagnosis of ulcerative colitis (UC) and Crohn’s disease (CD). Methods. One hundred and twenty patients with suspected diagnosis of IBD were studied. They were randomly divided into control group (routine CT examination) and observation group (low-dose CTE examination based on improved GIF algorithm), with 60 cases in each group. Comprehensive diagnosis was used as the standard to assess the diagnostic effect. Results. (1) The peak signal-to-noise ratio (PSNR) (26.02 dB) and structural similarity (SSIM) (0.8921) of the algorithm were higher than those of GIF (17.22 dB/0.8491), weighted guided image filtering (WGIF) (23.78 dB/0.8489), and gradient domain guided image filtering (GGIF) (23.77 dB/0.7567) ( P < 0.05 ); (2) the diagnostic sensitivity (91.49%), specificity (92.31%), accuracy (91.67%), positive predictive value (97.73%), and negative predictive value (75%) of the observation group were higher than those of the control group ( P < 0.05 ); the sensitivity and specificity of CTE in the diagnosis of UD and CD were 96.77% and 81.25% and 98.33% and 93.33%, respectively ( P < 0.05 ); there were significant differences in symmetrical intestinal wall thickening and smooth serosal surface between UD and CD ( P < 0.05 ). Conclusion. (1) The improved GIF algorithm has a more effective application value in the denoising processing of low-dose CT images and can better improve the image quality; (2) the accuracy of CTE in the diagnosis of IBD is high, and CTE is of great value in the differential diagnosis of UD and CD.

show abstract

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Algorithm-Based Differential Diagnosis of Crohn’s Disease and Ulcerative Colitis by CT Image

Jiang

Kuang

et al. 2022

Computational and Mathematical Methods in Medicine

View full text Add to dashboard Cite

show abstract

“…The gradient feature can be computed as the first-order directional derivative, quantifying the variation between pixels. In C-ETEM, we use the structure tensor (STO) (Wang and Wang, 2020 ) as an algorithm for local feature detection to measure the information of pixels with high activity levels in the high-frequency components. That is, Eq.…”

Section: Methodsmentioning

confidence: 99%

“…where STO (•) represents the STO measurement operator (Wang and Wang, 2020). Table 3 presents the quantitative comparison results of the three methods, where the maximum values of all metric scores are highlighted in bold.…”

Section: C-etemmentioning

confidence: 99%

Multimodal brain image fusion based on error texture elimination and salient feature detection

Li¹,

Li²

2023

Front. Neurosci.

View full text Add to dashboard Cite

As an important clinically oriented information fusion technology, multimodal medical image fusion integrates useful information from different modal images into a comprehensive fused image. Nevertheless, existing methods routinely consider only energy information when fusing low-frequency or base layers, ignoring the fact that useful texture information may exist in pixels with lower energy values. Thus, erroneous textures may be introduced into the fusion results. To resolve this problem, we propose a novel multimodal brain image fusion algorithm based on error texture removal. A two-layer decomposition scheme is first implemented to generate the high- and low-frequency subbands. We propose a salient feature detection operator based on gradient difference and entropy. The proposed operator integrates the gradient difference and amount of information in the high-frequency subbands to effectively identify clearly detailed information. Subsequently, we detect the energy information of the low-frequency subband by utilizing the local phase feature of each pixel as the intensity measurement and using a random walk algorithm to detect the energy information. Finally, we propose a rolling guidance filtering iterative least-squares model to reconstruct the texture information in the low-frequency components. Through extensive experiments, we successfully demonstrate that the proposed algorithm outperforms some state-of-the-art methods. Our source code is publicly available at https://github.com/ixilai/ETEM.

show abstract

“…They solved the Poisson equation at each resolution to achieve boundary continuity in the gradient domain. Wang et al [44] proposed a gradient domain image fusion method based on the structure tensor, in which source images were stacked into a multi-valued image, and the structure tensor of each source image was calculated according to its gradient graph.…”

Section: Gradient Domain (Gd)-based Methodsmentioning

confidence: 99%

A Survey of Multi-Focus Image Fusion Methods

Zhou

Zhi

et al. 2022

Applied Sciences

View full text Add to dashboard Cite

As an important branch in the field of image fusion, the multi-focus image fusion technique can effectively solve the problem of optical lens depth of field, making two or more partially focused images fuse into a fully focused image. In this paper, the methods based on boundary segmentation was put forward as a group of image fusion method. Thus, a novel classification method of image fusion algorithms is proposed: transform domain methods, boundary segmentation methods, deep learning methods, and combination fusion methods. In addition, the subjective and objective evaluation standards are listed, and eight common objective evaluation indicators are described in detail. On the basis of lots of literature, this paper compares and summarizes various representative methods. At the end of this paper, some main limitations in current research are discussed, and the future development of multi-focus image fusion is prospected.

show abstract

Fusion of 3-D medical image gradient domain based on detail-driven and directional structure tensor

Cited by 6 publications

References 35 publications

Artificial Intelligence Algorithm-Based Differential Diagnosis of Crohn’s Disease and Ulcerative Colitis by CT Image

Artificial Intelligence Algorithm-Based Differential Diagnosis of Crohn’s Disease and Ulcerative Colitis by CT Image

Multimodal brain image fusion based on error texture elimination and salient feature detection

A Survey of Multi-Focus Image Fusion Methods

Contact Info

Product

Resources

About