Tri-modal medical image fusion based on adaptive energy choosing scheme and sparse representation

• Published in: Measurement : journal of the International Measurement Confederation Vol. 204; p. 112038
• Main Authors: Jie, Yuchan, Zhou, Fuqiang, Tan, Haishu, Wang, Gao, Cheng, Xiaoqi, Li, Xiaosong
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 30.11.2022
• Subjects: Adaptive energy choosing; Cartoon-texture decomposition; Sparse representation; Tri-modal medical image fusion; Tri-modal medical image fusion; Sparse representation; Cartoon-texture decomposition; Adaptive energy choosing
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2022.112038

•Tri-modal image fusion can be improved using cartoon-texture decomposition.•A novel scheme is proposed to fuse cartoon components while retaining brightness.•The proposed method shows excellent subjective and objective evaluation results. Multimodal medical image fusion integrates useful information from multiple single-modal medical images, generating a more comprehensive and objective fused image that better assist clinical applications. In this paper, a novel tri-modal medical image fusion method based on cartoon-texture decomposition is proposed and performed using a rolling guidance filter, and sparse representation, to fuse the texture components. Furthermore, a novel adaptive energy choosing scheme is proposed to fuse the cartoon components; through this approach, the brightness of cartoon components can be effectively detected. Finally, the fused image is reconstructed by combining the fused texture and cartoon components. Experimental results demonstrate that the proposed method yields better performance than some state-of-the-art methods in subjective and objective assessments. Meanwhile, the average level of the proposed method are 28.44%, 8.94%, 0.07%, 16.09%, 58.66%, and 0.34% higher than the compared methods evaluated by the metrics including QMI, QTE, QNCIE, QG, QP and EN, respectively.