Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)

Medical Image Super-Resolution Reconstruction Algorithms on Deep Learning

Authors
Jinglin Yuan1, *
1School of Applied Sciences, Macao Polytechnic University, Macau, China
*Corresponding author.
Corresponding Author
Jinglin Yuan
Available Online 14 February 2024.
DOI
10.2991/978-94-6463-370-2_41How to use a DOI?
Keywords
Medical image; Super-resolution reconstruction; Convolutional neural networks; MSID; NSST
Abstract

The human body’s structural details can be more clearly seen in high-resolution MRI and CT pictures, which can also aid in the early identification of disorders. However, due to the limits of imaging technologies, imaging surroundings, and human variables, clean high-resolution photographs are challenging to obtain. For super-resolution reconstruction of medical pictures, I propose a non-subsampled shearlet transform (NSST) and multi-scale information distillation (MSID) network in the current study namely NSST-MSID network. In order to thoroughly investigate the multiscale aspects of images and successfully restore low-resolution photos to high-resolution images, an MSID network that primarily comprises of several cascaded MSID blocks is first proposed. In addition, the super-resolution problem of medical images is characterized as a prediction problem of NSST coefficients, so that the MSID network maintains richer structural details than the spatial domain. This is because existing methods frequently predict high-resolution images in the spatial domain, making the output too smooth and texture details lost. The performance of the suggested strategy is then assessed using the well-known medical image dataset. In comparison to other outstanding methods currently in use, the experimental results demonstrate that the NSST-MSID network can achieve better peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and root-mean-square error (RMSE) values while better preserving local texture details and global topology.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)
Series
Advances in Intelligent Systems Research
Publication Date
14 February 2024
ISBN
978-94-6463-370-2
ISSN
1951-6851
DOI
10.2991/978-94-6463-370-2_41How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

TY  - CONF
AU  - Jinglin Yuan
PY  - 2024
DA  - 2024/02/14
TI  - Medical Image Super-Resolution Reconstruction Algorithms on Deep Learning
BT  - Proceedings of the 2023 International Conference on Data Science, Advanced Algorithm and Intelligent Computing (DAI 2023)
PB  - Atlantis Press
SP  - 389
EP  - 399
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-370-2_41
DO  - 10.2991/978-94-6463-370-2_41
ID  - Yuan2024
ER  -