Salt-and-pepper Noise Reduction for Medical Images Based on Image Fusion

Shixiao Wu; Chengcheng Guo; Xinghuan Wang

doi:10.2174/1573405620666230525104841

Salt-and-pepper Noise Reduction for Medical Images Based on Image Fusion

Curr Med Imaging. 2023 May 25. doi: 10.2174/1573405620666230525104841. Online ahead of print.

Authors

Shixiao Wu^{1

2}, Chengcheng Guo¹, Xinghuan Wang³

Affiliations

¹ Communication and Information System, School of Electronic Information, Wuhan University, Wuhan, China.
² School of Information Engineering, Wuhan Business University, Wuhan, China.
³ Urology, Zhongnan Hospital of Wuhan University, Wuhan, China.

PMID: 37231763
DOI: 10.2174/1573405620666230525104841

Abstract

Background: During the collection process, the prostate capsule is prone to introduce salt and pepper noise due to gastrointestinal peristalsis, which will affect the precision of subsequent object detection.

Objective: A cascade optimization scheme for image denoising based on image fusion was proposed to improve the peak signal-to-noise ratio(PSNR) and contour protection performance of heterogeneous medical images after image denoising.

Method: Anisotropic diffusion fusion (ADF) was used to decompose the images denoised by adaptive median filter, non-local adaptive median filter and artificial neural network to generate the base layer and detail layer, which were fused by weighted average and Karhunen-Loeve Transform respectively. Finally, the image was reconstructed by linear superposition.

Results: Compared with the traditional denoising method, the image denoised by this method has a higher PSNR while maintaining the image edge contour.

Conclusion: Using the denoised dataset for object detection, the detection precision of the obtained model is higher.

Keywords: AMF; Anisotropic diffusion fusion; Artificial neural network; Non-local adaptive medican filter; denoised; gastrointestinal peristalsis.