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Volume 8 Issue 5
May  2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2021  >  8(5): 1025-1037
C. C. Leng, H. Zhang, G. R. Cai, Z. Chen, and A. Basu, "Total Variation Constrained Non-Negative Matrix Factorization for Medical Image Registration," IEEE/CAA J. Autom. Sinica, vol. 8, no. 5, pp. 1025-1037, May. 2021. doi: 10.1109/JAS.2021.1003979
Citation: C. C. Leng, H. Zhang, G. R. Cai, Z. Chen, and A. Basu, "Total Variation Constrained Non-Negative Matrix Factorization for Medical Image Registration," IEEE/CAA J. Autom. Sinica, vol. 8, no. 5, pp. 1025-1037, May. 2021. doi: 10.1109/JAS.2021.1003979
shu

Total Variation Constrained Non-Negative Matrix Factorization for Medical Image Registration

doi: 10.1109/JAS.2021.1003979
  • 1.

    School of Mathematics, Northwest University, Xi’an 710127, China

  • 2.

    Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

  • 3.

    Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada

  • 4.

    College of Computer Engineering, Jimei University, Xiamen 361021, China

  • 5.

    School of Measuring and Optical Engineering, Nanchang Hangkong University, Nanchang 330063, China

  • 6.

    Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada

Funds:  This work was supported by the National Natural Science Foundation of China (61702251, 41971424, 61701191, U1605254), the Natural Science Basic Research Plan in Shaanxi Province of China (2018JM6030), the Key Technical Project of Fujian Province (2017H6015), the Science and Technology Project of Xiamen (3502Z20183032), the Doctor Scientific Research Starting Foundation of Northwest University (338050050), Youth Academic Talent Support Program of Northwest University (360051900151), and the Natural Sciences and Engineering Research Council of Canada, Canada
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    Abstract
  • This paper presents a novel medical image registration algorithm named total variation constrained graph-regularization for non-negative matrix factorization (TV-GNMF). The method utilizes non-negative matrix factorization by total variation constraint and graph regularization. The main contributions of our work are the following. First, total variation is incorporated into NMF to control the diffusion speed. The purpose is to denoise in smooth regions and preserve features or details of the data in edge regions by using a diffusion coefficient based on gradient information. Second, we add graph regularization into NMF to reveal intrinsic geometry and structure information of features to enhance the discrimination power. Third, the multiplicative update rules and proof of convergence of the TV-GNMF algorithm are given. Experiments conducted on datasets show that the proposed TV-GNMF method outperforms other state-of-the-art algorithms.

     

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    Highlights

    • This paper presents a novel image registration algorithm named Total Variation constrained Graph-regularization for Non-negative Matrix Factorization (TV-GNMF).
    • Total variation is incorporated into NMF to denoise in smooth regions and preserve the features or details of data in edge regions.
    • Graph regularization is added into NMF to reveal intrinsic geometry and structure information of data to enhance the discrimination power.
    • The multiplicative update rules and proof of convergence of the TV-GNMF algorithm are given.
    • Experimental results show that the proposed TV-GNMF method outperforms other state-of-the-art algorithms.

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