Medical Image Despeckling Using the Invertible Sparse Fuzzy Wavelet Transform with Nature-Inspired Minibatch Water Wave Swarm Optimization

Ahila Amarnath¹, Poongodi Manoharan², Buvaneswari Natarajan³, Roobaea Alroobaea⁴, Majed Alsafyani⁴, Abdullah M Baqasah⁵, Ismail Keshta⁶, Kaamran Raahemifar^{7

8

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Affiliations

¹ Indian Institute of Technology, Madras, Chennai 600036, Tamilnadu, India.
² College of Science and Engineering, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar.
³ Middlesex College, Edison, NJ 08818, USA.
⁴ Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
⁵ Department of Information Technology, College of Computers and Information Technology, Taif University, Taif 21974, Saudi Arabia.
⁶ Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh 11597, Saudi Arabia.
⁷ Data Science and Artificial Intelligence Program, College of Information Sciences and Technology, Penn State University, State College, PA 16801, USA.
⁸ School of Optometry and Vision Science, Faculty of Science, University of Waterloo, 200 University, Waterloo, ON N2L3G1, Canada.
⁹ Faculty of Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3E9, Canada.

PMID: 37761285
PMCID: PMC10529025
DOI: 10.3390/diagnostics13182919

Medical Image Despeckling Using the Invertible Sparse Fuzzy Wavelet Transform with Nature-Inspired Minibatch Water Wave Swarm Optimization

Ahila Amarnath et al. Diagnostics (Basel). 2023.

. 2023 Sep 12;13(18):2919.

doi: 10.3390/diagnostics13182919.

Authors

Ahila Amarnath¹, Poongodi Manoharan², Buvaneswari Natarajan³, Roobaea Alroobaea⁴, Majed Alsafyani⁴, Abdullah M Baqasah⁵, Ismail Keshta⁶, Kaamran Raahemifar^{7

8

9}

Affiliations

¹ Indian Institute of Technology, Madras, Chennai 600036, Tamilnadu, India.
² College of Science and Engineering, Hamad Bin Khalifa University, Doha P.O. Box 34110, Qatar.
³ Middlesex College, Edison, NJ 08818, USA.
⁴ Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia.
⁵ Department of Information Technology, College of Computers and Information Technology, Taif University, Taif 21974, Saudi Arabia.
⁶ Computer Science and Information Systems Department, College of Applied Sciences, AlMaarefa University, Riyadh 11597, Saudi Arabia.
⁷ Data Science and Artificial Intelligence Program, College of Information Sciences and Technology, Penn State University, State College, PA 16801, USA.
⁸ School of Optometry and Vision Science, Faculty of Science, University of Waterloo, 200 University, Waterloo, ON N2L3G1, Canada.
⁹ Faculty of Engineering, University of Waterloo, 200 University Ave W, Waterloo, ON N2L 3E9, Canada.

PMID: 37761285
PMCID: PMC10529025
DOI: 10.3390/diagnostics13182919

Abstract

Speckle noise is a pervasive problem in medical imaging, and conventional methods for despeckling often lead to loss of edge information due to smoothing. To address this issue, we propose a novel approach that combines a nature-inspired minibatch water wave swarm optimization (NIMWVSO) framework with an invertible sparse fuzzy wavelet transform (ISFWT) in the frequency domain. The ISFWT learns a non-linear redundant transform with a perfect reconstruction property that effectively removes noise while preserving structural and edge information in medical images. The resulting threshold is then used by the NIMWVSO to further reduce multiplicative speckle noise. Our approach was evaluated using the MSTAR dataset, and objective functions were based on two contrasting reference metrics, namely the peak signal-to-noise ratio (PSNR) and the mean structural similarity index metric (MSSIM). Our results show that the suggested approach outperforms modern filters and has significant generalization ability to unknown noise levels, while also being highly interpretable. By providing a new framework for despeckling medical images, our work has the potential to improve the accuracy and reliability of medical imaging diagnosis and treatment planning.

Keywords: inveritible sparse fuzzy wavelet transform; nature-inspired minibatch water wave swarm optimization; speckle noise; threshold.

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Conflict of interest statement

The Authors declare no conflict of interest regarding the publication of this research paper.

Figures

**Figure 1**
Schematic representation of the suggested methodology.

**Figure 4**
Image sample vs. noise amplitude.

**Figure 5**
Performance analysis of the suggested mechanism.

See this image and copyright information in PMC

References

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Medical Image Despeckling Using the Invertible Sparse Fuzzy Wavelet Transform with Nature-Inspired Minibatch Water Wave Swarm Optimization

Affiliations

Medical Image Despeckling Using the Invertible Sparse Fuzzy Wavelet Transform with Nature-Inspired Minibatch Water Wave Swarm Optimization

Authors

Affiliations

Abstract

Conflict of interest statement

Figures

References

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