Article ; Online: Tensor Sparse Representation for 3-D Medical Image Fusion Using Weighted Average Rule.
IEEE transactions on bio-medical engineering
2018 Volume 65, Issue 11, Page(s) 2622–2633
Abstract: Objective: The technique of fusing multimodal medical images into single image has a great impact on the clinical diagnosis. The previous works mostly concern the two-dimensional (2-D) image fusion performed on each slice individually, that may destroy ... ...
Abstract | Objective: The technique of fusing multimodal medical images into single image has a great impact on the clinical diagnosis. The previous works mostly concern the two-dimensional (2-D) image fusion performed on each slice individually, that may destroy the 3-D correlation across adjacent slices. To address this issue, this paper proposes a novel 3-D image fusion scheme based on Tensor Sparse Representation (TSR). Methods: First, each medical volume is arranged as a three-order tensor, and represented by TSR with learned dictionaries. Second, a novel "weighted average" rule, calculated from the tensor sparse coefficients using 3-D local-to-global strategy. The weights are then employed to combine the multimodal medical volumes through weighted average. Results: The visual and objective comparisons show that the proposed method is competitive to the existing methods on various medical volumes in different imaging modalities. Conclusion: The TSR-based 3-D fusion approach with weighted average rule can preserve the 3-D structure of medical volume, and reduce the low contrast and artifacts in fused product. Significance: The designed weights offer the effective assigned weights and accurate salience levels measure, which can improve the performance of fusion approach. |
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MeSH term(s) | Algorithms ; Brain/diagnostic imaging ; Humans ; Imaging, Three-Dimensional/methods ; Magnetic Resonance Imaging ; Multimodal Imaging/methods ; Positron-Emission Tomography |
Language | English |
Publishing date | 2018-02-28 |
Publishing country | United States |
Document type | Journal Article ; Research Support, Non-U.S. Gov't |
ZDB-ID | 160429-6 |
ISSN | 1558-2531 ; 0018-9294 |
ISSN (online) | 1558-2531 |
ISSN | 0018-9294 |
DOI | 10.1109/TBME.2018.2811243 |
Database | MEDical Literature Analysis and Retrieval System OnLINE |
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