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

Abstract

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.