Abstract
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.
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Acknowledgements
This work was supported by the National Nature Science Foundation of China under Grant number 61801438, the Science and Technology Innovation Project of Colleges and Universities of Shanxi Province under Grant (2020L0282, 2020L0595), the Natural Science Foundation of Shanxi province of China under Grant (201901D111161, 201901D111153).
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Kang, J., Gui, Z., Liu, Y. et al. LDCT image quality improvement algorithm based on optimal wavelet basis and MCA. SIViP 16, 2303–2311 (2022). https://doi.org/10.1007/s11760-022-02196-1
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DOI: https://doi.org/10.1007/s11760-022-02196-1