Title:Salt-and-pepper Noise Reduction for Medical Images based on Image Fusion
Volume: 20
Author(s): Shixiao Wu*, Chengcheng Guo and Xinghuan Wang
Affiliation:
- Department of Communication and Information System, School of Electronic Information, Wuhan University, Wuhan, China
- School of Information Engineering, Wuhan Business University, Wuhan, China
Keywords:
AMF, Artificial neural network, Anisotropic diffusion fusion, Non-local adaptive medican filter, Denoised, Gastrointestinal peristalsis.
Abstract:
Background:
During the collection process, the prostate capsula 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.
Methods:
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.
