Medical Image Fusion in Gradient Domain with Structure Tensor

BACKGROUND: Multi-modal medical image fusion plays a crucial role in many areas of modern medicine like diagnosis and therapy planning. OBJECTIVE: Due to the factor that the structure tensor has the property of preserving the image geometry, we utilized it to construct the directional structure tensor and further proposed an improved 3-D medical image fusion method. METHOD: The local entropy metrics were used to construct the gradient weights of different source images, and the eigenvectors of traditional structure tensor were combined with the second-order derivatives of image to construct the directional structure tensor. In addition, the guided filtering was employed to obtain detail components of the source images and construct a fused gradient field with the enhanced detail. Finally, the fusion image was generated by solving the functional minimization problem. RESULTS AND CONCLUSION: Experimental results demonstrated that this new method is superior to the traditional structure tensor and multi-scale analysis in both visual effect and quantitative assessment.

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Medical image fusion based on variational and nonlinear structure tensor

MATEC Web of Conferences ◽

10.1051/matecconf/201818910021 ◽

2018 ◽

Vol 189 ◽

pp. 10021

Author(s):

Xiaobei Wang ◽

Rencan Nie ◽

Xiaopeng Guo

Keyword(s):

Image Fusion ◽

Medical Image ◽

Energy Functional ◽

Structure Tensor ◽

Fusion Method ◽

Edge Information ◽

Medical Image Fusion ◽

Fused Image ◽

New Energy ◽

Fusion Methods

Medical image fusion plays an important role in detection and treatment of disease. Although numerous medical image fusion methods have been proposed, most of them decrease the contrast and lose the image information. In this paper, a novel MRI and CT image fusion method is proposed combining rolling guidance filter, structure tensor, and nonsubsampled shearlet transform (NSST). First, the rolling guidance filter and the sum-modified laplacian (SML) operator are introduced in the algorithm to construct the weight maps in non-linear domain, then the fused gradient is firstly obtained by a new weighted structure tensor fusion method, and the fused image is firstly acquired in NSST domain, finally, a new energy functional is defined to constrain the gradient and pixel information of the final fused image close to the pre-fused gradient and the pre-fused image, experimental results show that the proposed method can retain the edge information of source images effectively and preserve the reduction of contrast.

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Multi-Modal Medical Image Fusion Based on Gradient Domain Guided Image Filter

Recent Advances in Computer Based Systems, Processes and Applications ◽

10.1201/9781003043980-14 ◽

2020 ◽

pp. 107-115

Author(s):

K. Vanitha ◽

D. Satyanarayana ◽

M. N. Giri Prasad

Keyword(s):

Image Fusion ◽

Medical Image ◽

Image Filter ◽

Medical Image Fusion ◽

Guided Image Filter ◽

Gradient Domain

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Multimodal Medical Image Fusion Using Gradient Domain Guided Filter Random Walk and Side Window Filtering in Framelet Domain

Information Sciences ◽

10.1016/j.ins.2021.11.033 ◽

2021 ◽

Author(s):

Weiwei Kong ◽

Qiguang Miao ◽

Ruyi Liu ◽

Yang Lei ◽

Jing Cui ◽

...

Keyword(s):

Random Walk ◽

Image Fusion ◽

Medical Image ◽

Guided Filter ◽

Side Window ◽

Medical Image Fusion ◽

Gradient Domain

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Medical Images Fusion with Patch Based Structure Tensor

The Open Biomedical Engineering Journal ◽

10.2174/1874120701509010199 ◽

2015 ◽

Vol 9 (1) ◽

pp. 199-203

Author(s):

Fen Luo ◽

Jiangfeng Sun ◽

Shouming Hou

Keyword(s):

Image Fusion ◽

Medical Image ◽

Structure Tensor ◽

Mr Images ◽

Wavelet Theory ◽

Medical Image Fusion ◽

Source Data ◽

Eigen Values ◽

Input Source ◽

Suppress Noise

Nowadays medical imaging has played an important role in clinical use, which provide important clues for medical diagnosis. In medical image fusion, the extraction of some fine details and description is critical. To solve this problem, a modified structure tensor by considering similarity between two patches is proposed. The patch based filter can suppress noise and add the robustness of the eigen-values of the structure tensor by allowing the use of more information of far away pixels. After defining the new structure tensor, we apply it into medical image fusion with a multi-resolution wavelet theory. The features are extracted and described by the eigen-values of two multi-modality source data. To test the performance of the proposed scheme, the CT and MR images are used as input source images for medical image fusion. The experimental results show that the proposed method can produce better results compared to some related approaches.

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