Research article Special Issues

A novel multi-modal fundus image fusion method for guiding the laser surgery of central serous chorioretinopathy


  • Received: 23 April 2021 Accepted: 25 May 2021 Published: 02 June 2021
  • The angiography and color fundus images are of great assistance for the localization of central serous chorioretinopathy (CSCR) lesions. However, it brings much inconvenience to ophthalmologists because of these two modalities working independently in guiding laser surgery. Hence, a novel fundus image fusion method in non-subsampled contourlet transform (NSCT) domain, aiming to integrate the multi-modal CSCR information, is proposed. Specifically, the source images are initially decomposed into high-frequency and low-frequency components based on NSCT. Then, an improved deep learning-based method is employed for the fusion of low-frequency components, which helps to alleviate the tedious process of manually designing fusion rules and enhance the smoothness of the fused images. The fusion of high-frequency components based on pulse-coupled neural network (PCNN) is closely followed to facilitate the integration of detailed information. Finally, the fused images can be obtained by applying an inverse transform on the above fusion components. Qualitative and quantitative experiments demonstrate the proposed scheme is superior to the baseline methods of multi-scale transform (MST) in most cases, which not only implies its potential in multi-modal fundus image fusion, but also expands the research direction of MST-based fusion methods.

    Citation: Jianguo Xu, Cheng Wan, Weihua Yang, Bo Zheng, Zhipeng Yan, Jianxin Shen. A novel multi-modal fundus image fusion method for guiding the laser surgery of central serous chorioretinopathy[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 4797-4816. doi: 10.3934/mbe.2021244

    Related Papers:

  • The angiography and color fundus images are of great assistance for the localization of central serous chorioretinopathy (CSCR) lesions. However, it brings much inconvenience to ophthalmologists because of these two modalities working independently in guiding laser surgery. Hence, a novel fundus image fusion method in non-subsampled contourlet transform (NSCT) domain, aiming to integrate the multi-modal CSCR information, is proposed. Specifically, the source images are initially decomposed into high-frequency and low-frequency components based on NSCT. Then, an improved deep learning-based method is employed for the fusion of low-frequency components, which helps to alleviate the tedious process of manually designing fusion rules and enhance the smoothness of the fused images. The fusion of high-frequency components based on pulse-coupled neural network (PCNN) is closely followed to facilitate the integration of detailed information. Finally, the fused images can be obtained by applying an inverse transform on the above fusion components. Qualitative and quantitative experiments demonstrate the proposed scheme is superior to the baseline methods of multi-scale transform (MST) in most cases, which not only implies its potential in multi-modal fundus image fusion, but also expands the research direction of MST-based fusion methods.



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