Multi-modal Medical Image Fusion based on Two-scale Image Decomposition and Sparse Representation

Abstract Multimodality image fusion is the hot topic in medical imaging field which increases the clinical diagnosis accuracy through fusing complementary information of multimodality images. In this paper, a multimodal image fusion scheme is introduced based on two-scale image decomposition and sparse representation. In the proposed scheme, the source multimodal images are first processed through contrast enhancement technique so that the intensity distribution is improved for better visualization. A spatial gradient based edge detection technique is used for extracting the edge information from contrast stretched images. The enhanced multimodality images are then decomposed into two components: the base and detail layers. The final detail layer is extracted by using SSGSM. Finally, by using an enhanced decision maps and fusion scheme the fused image is obtained. The experimental results show that the proposed multimodal image fusion scheme outperforms with some others methods by performing qualitative and quantitative analysis.