Tensor Sparse Representation for 3-D Medical Image Fusion Using Weighted Average Rule

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.