Multimodal Medical Image Fusion Based on Parameter Adaptive PCNN and Latent Low-Rank Representation
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Nanjing University of Information Science and Technology

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    Abstract:

    Medical image fusion has been developed as an efficient assistive technology in various clinical applications such as medical diagnosis and treatment planning. Aiming at the problem of insufficient protection of image contour and detail information by traditional image fusion methods, a new multimodal medical image fusion method is proposed. This method first uses non-subsampled shearlet transform to decompose the source image to obtain high and low frequency subband coefficients, then uses the latent low rank representation algorithm to fuse the low frequency subband coefficients, and applies the improved PAPCNN algorithm to fuse the high frequency subband coefficients. Finally, based on the automatic setting of parameters, the optimization method configuration of the time decay factor 𝛼𝑒 is carried out. The experimental results show that the proposed method solves the problems of difficult parameter setting and insufficient detail protection ability in traditional PCNN algorithm fusion images, and at the same time, it has achieved great improvement in visual quality and objective evaluation indicators.

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History
  • Received:May 07,2023
  • Revised:May 07,2023
  • Adopted:May 16,2023
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  • Copyright (c) 2023 by the authors. This work is licensed under a Creative
  • Creative Commons Attribution-ShareAlike 4.0 International License.