LDCT image quality improvement algorithm based on optimal wavelet basis and MCA

This paper puts forward a denoising algorithm for low-dose computed tomography (LDCT) image based on optimal wavelet basis and morphological component analysis, which aims to solve the problem of severe noise and artifacts in LDCT imaging. First, the high-frequency (HF) component coefficients in the...

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Bibliographic Details
Published in:Signal, image and video processing image and video processing, 2022-11, Vol.16 (8), p.2303-2311
Main Authors: Kang, Jiaqi, Gui, Zhiguo, Liu, Yi, Wang, Zhenyu, Li, Zhiyuan, Lu, Jing, Zhang, Quan
Format: Article
Language:English
Subjects:
Algorithms
Coefficients
Computed tomography
Computer Imaging
Computer Science
Image Processing and Computer Vision
Image quality
Machine learning
Multimedia Information Systems
Noise reduction
Original Paper
Pattern Recognition and Graphics
Signal,Image and Speech Processing
Vision
Wavelet analysis
Wavelet transforms
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Summary:This paper puts forward a denoising algorithm for low-dose computed tomography (LDCT) image based on optimal wavelet basis and morphological component analysis, which aims to solve the problem of severe noise and artifacts in LDCT imaging. First, the high-frequency (HF) component coefficients in the horizontal, vertical, and diagonal directions of LDCT after the stationary wavelet transform (SWT) are weighted to obtain the wavelet basis selection coefficients, and the wavelet basis with the smallest wavelet select coefficient is selected as the optimal wavelet basis. Second, the artifacts are processed using the MCA algorithm based on online dictionary learning (ODL) for the HF component. Third, the improved LDCT images are obtained using the inverse stationary wavelet transform (ISWT), which uses the low-frequency (LF) components and the denoised HF component. The extensive experiments on simulated and real data demonstrated the images denoised using the optimal wavelet basis algorithm showed the highest objective evaluation index, followed by the other wavelet-based algorithms. Additionally, our proposed method outperformed several classical denoising methods on both quantitative and qualitative assessments. It was therefore verified that the validity of wavelet selection and the feasibility of the proposed algorithm.
ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-022-02196-1