Joint sparse model with coupled dictionary for medical image fusion. (January 2023)

• Record Type: Journal Article
• Authors: Zhang, Chengfang; Zhang, Ziyou; Feng, Ziliang; Yi, Liangzhong
• Is Part Of: Biomedical signal processing and control. Volume 79(2023)Part 1
• Publication Date: 2023-01
• Subjects: Medical image fusion -- Coupled dictionary -- Joint sparse model -- Structural information -- Functional 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.2022.104030
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract: Medical image fusion has made great progress benefit from strong feature association ability of joint sparse model. Since medical images are obtained by different sensors with different imaging mechanisms, single dictionary cannot effectively characterize innovation and common information, which becomes stumbling block for effective fusion. In this paper, we propose novel joint sparse model with coupled dictionary learning for medical image fusion. Our framework design new fusion rule to enhance ability of multi-source signal preservation and retain edge/texture information. Firstly, source medical images was represented as a common sparse component and innovation sparse components with over-complete coupled dictionaries. Secondly, fused sparse coefficient is obtained by using the designed novel rule. Finally, fused results are reconstructed using fused coefficients and coupled dictionaries. Our proposed method is evaluated and compared with 9 state-of-the-art fusion methods on public Harvard dataset. Qualitative and quantitative results show that our method is generally better than the compared methods in terms of retaining source image structural and functional information. Highlights: A novel medical image fusion framework is proposed to alleviate defect of joint sparse model with single dictionary. A suitable fusion strategy are designed to highlight and enrich similar information of source images. Proposed method improve time-effectiveness by reducing the loss of functional and structural information.