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

Abstract

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). 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. The visual and objective comparisons show that the proposed method is competitive to the existing methods on various medical volumes in different imaging modalities. 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. The designed weights offer the effective assigned weights and accurate salience levels measure, which can improve the performance of fusion approach.