Multimodal medical image fusion using gradient domain guided filter random walk and side window filtering in framelet domain

Kong, Weiwei; Miao, Qiguang; Liu, Ruyi; Lei, Yang; Cui, Jing; Xie, Qiang

doi:10.1016/j.ins.2021.11.033

Cited by 14 publications

(3 citation statements)

References 44 publications

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“…This technique has been widely applied in many fields, such as land cover mapping 1,2 , object detection [3][4][5] and medical diagnosis 6-8. Medical image fusion is to synthesize multiple medical images from single or different imaging devices. The main purpose of the medical image fusion is to improve imaging quality while preserving the specific features for accurate diagnosis and treatment, which plays an important role in surgical navigation, routine staging, and radio-therapy planning of malignant disease [9][10][11][12][13][14] .…”

Section: Introductionmentioning

confidence: 99%

Medical Image Fusion with Deep Neural Networks

Liang

2023

Preprint

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Medical image fusion aims to fuse multiple images from a single or multiple imaging modes to enhance their corresponding clinical applications in diagnosing and evaluating medical problems, a trend that has attracted increasing attention. However, most recent medical image fusion methods require prior knowledge, making it difficult to select image features. In this paper, we propose a novel deep medical image fusion method based on a deep convolutional neural network (DCNN) for directly learning image features from original images. Specifically, source images are first decomposed by low rank representation to obtain the principal and salient components, respectively. Following that, the deep features are extracted from the decomposed principal components via DCNN and fused by a weighted-average rule. Then, considering the complementary between the salient components obtained by the low rank representation, a simple yet effective sum rule is designed to fuse the salient components. Finally, the fused result is obtained by reconstructing the principal and salient components. The experimental results demonstrate that the proposed method outperforms several state-of-the-art medical image fusion approaches in terms of both objective indices and visual quality.

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Section: Introductionmentioning

confidence: 99%

Medical Image Fusion with Deep Neural Networks

Liang

2023

Preprint

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“…To authenticate access authentication, label information needs to be embedded into the encrypted data. In the medical field, multimodal medical images [4] are usually encrypted to prevent information on the patient's condition from leaking. For convenience of management, the information about individuals and conditions are embedded into encrypted images [5].…”

Section: Introductionmentioning

confidence: 99%

A Multi-Domain Embedding Framework for Robust Reversible Data Hiding Scheme in Encrypted Videos

et al. 2022

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For easier cloud management, reversible data hiding is performed in an encrypted domain to embed label information. However, the existing schemes are not robust and may cause the loss of label information during transmission. Enhancing robustness while maintaining reversibility in data hiding is a challenge. In this paper, a multi-domain embedding framework in encrypted videos is proposed to achieve both robustness and reversibility. In the framework, the multi-domain characteristic of encrypted video is fully used. The element for robust embedding is encrypted through Logistic chaotic scrambling, which is marked as element-Ⅰ. To further improve robustness, the label information will be encoded with the Bose–Chaudhuri–Hocquenghem code. Then, the label information will be robustly embedded into element-Ⅰ by modulating the amplitude of element-Ⅰ, in which the auxiliary information is generated for lossless recovery of the element-Ⅰ. The element for reversible embedding is marked as element-II, the sign of which will be encrypted by stream cipher. The auxiliary information will be reversibly embedded into element-Ⅱ through traditional histogram shifting. To verity the feasibility of the framework, an anti-recompression RDH-EV based on the framework is proposed. The experimental results show that the proposed scheme outperforms the current representative ones in terms of robustness, while achieving reversibility. In the proposed scheme, video encryption and data hiding are commutative and the original video bitstream can be recovered fully. These demonstrate the feasibility of the multi-domain embedding framework in encrypted videos.

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“…In a broad sense, the concept of multi-scale analysis is not limited to the above wavelets analysis and ultra-wavelet analysis. In addition, there are also image fusion strategies based on multi-scale analysis or approximate multi-scale analysis, such as Laplace pyramid [17], guide filter [18,19], multi-stage edge protection filter [20], multi-level potential low-rank representation mdlatlrr [21], significant multi-scale [22], empirical wavelet [23], and frame set transformation [24]. Because the concept of multi-scale analysis has an open extension, more scholars are keen on imaging research based on the multi-scale analysis.…”

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confidence: 99%

Image fusion research based on the Haar-like multi-scale analysis

Zhu,

Wen

2024

EURASIP J. Adv. Signal Process.

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In view of the serious color and definition distortion in the process of the traditional image fusion, this study proposes a Haar-like multi-scale analysis model, in which Haar wavelet has been modified and used for the medical image fusion to obtain even better results. Firstly, when the improved Haar wavelet basis function is translated, inner product and down-sampled with each band of the original image, the band is decomposed into four sub-images containing one low-frequency subdomain and three high-frequency subdomains. Secondly, the different fusion rules are applied in the low-frequency domain and the high-frequency domains to get the low-frequency sub-image and the high-frequency sub-images in each band. The four new sub-frequency domains are inverse-decomposed to reconstruct each new band. The study configures and synthesizes these new bands to produce a fusion image. Lastly, the two groups of the medical images are used for experimental simulation. The Experimental results are analyzed and compared with those of other fusion methods. It can be found the fusion method proposed in the study obtain the superior effects in the spatial definition and the color depth feature, especially in color criteria such as OP, SpD, CR and SSIM, comparing with the other methods.

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Multimodal medical image fusion using gradient domain guided filter random walk and side window filtering in framelet domain

Cited by 14 publications

References 44 publications

Medical Image Fusion with Deep Neural Networks

Medical Image Fusion with Deep Neural Networks

A Multi-Domain Embedding Framework for Robust Reversible Data Hiding Scheme in Encrypted Videos

Image fusion research based on the Haar-like multi-scale analysis

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