Multi-modality medical image fusion based on image decomposition framework and nonsubsampled shearlet transform. (February 2018)

• Record Type: Journal Article
• Authors: Liu, Xingbin; Mei, Wenbo; Du, Huiqian
• Is Part Of: Biomedical signal processing and control. Volume 40(2018)
• Page Start: 343
• Page End: 350
• Publication Date: 2018-02
• Subjects: Medical image fusion -- Image decomposition framework -- Nonsubsampled shearlet transform -- Mutual information; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2017.10.001
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: MFDF combined with NSST is proposed for medical image fusion. The NSST and MFDF are introduced in the proposed scheme. Maximum selection fusion rule is employed to fuse texture components. The approximate components are merged using NSST. Experiments verify the superior performance of the proposed method. Abstract: Medical image fusion increases accuracy of clinical diagnosis and analysis through integrating complementary information of multi-modality medical images. A novel multi-modality medical image fusion algorithm exploiting a moving frame based decomposition framework (MFDF) and the nonsubsampled shearlet transform (NSST) is proposed. The MFDF is applied to decompose source images into texture components and approximation components. Maximum selection fusion rule is employed to fuse texture components aimed at transferring salient gradient information to the fused image. The approximate components are merged using NSST. Finally, a components synthesis process is adopted to produce the fused image. Experimental results verify that the proposed method achieves better performance than other compared state-of-art methods in both visual effects and objective criteria.