Non-convex penalty based multimodal medical image fusion via sparse tensor factorization

Haoze Sun; Xiao-Xue Deng; Zhenya Wang; Yan Yan; Guoxia Xu; Yu-Feng Yu

doi:10.1117/12.2606497

28 October 2021 Non-convex penalty based multimodal medical image fusion via sparse tensor factorization

Haoze Sun, Xiao-Xue Deng, Zhenya Wang, Yan Yan, Guoxia Xu, Yu-Feng Yu

Proceedings Volume 11884, International Symposium on Artificial Intelligence and Robotics 2021; 118841N (2021) https://doi.org/10.1117/12.2606497
Event: International Symposium on Artificial Intelligence and Robotics 2021, 2021, Fukuoka, Japan

Abstract

Nowadays, medical image fusion serves as a significant aid for the precise diagnosis or surgical navigation. In this paper, we propose a novel tensor factorization based fusion strategy which well combines the multimodal, multiscale nature of medical images and multiway structure of tensors. Since our model adopts the sparse representation (SR) prior, we suffer from the systematic underestimation of the true solution because of the L₁-norm regularization term. To address this problem, we introduce the generalized minimax-concave (GMC) penalty into our framework, which is a non-convex regularization term itself. It is beneficial for the whole cost function to maintain convexity. Furthermore, we combine the alternating direction method of multipliers (ADMM) algorithm and forward-backward (FB) method to achieve the optimization process. We conduct extensive experiments on five kinds of practical medical image fusion problems with 96 pairs of images in total. The results confirm that our model has great improvements in visual performance and objective metrics against the existing methods.

Citation Download Citation

Haoze Sun, Xiao-Xue Deng, Zhenya Wang, Yan Yan, Guoxia Xu, and Yu-Feng Yu "Non-convex penalty based multimodal medical image fusion via sparse tensor factorization", Proc. SPIE 11884, International Symposium on Artificial Intelligence and Robotics 2021, 118841N (28 October 2021); https://doi.org/10.1117/12.2606497

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