An Adaptive Quality Improved Algorithm in Low Dose CT Images
[1]Jiang Huiqin,Xu Yufeng,Maling,et al.An Adaptive Quality Improved Algorithm in Low Dose CT Images[J].Journal of Zhengzhou University (Engineering Science),2018,39(04):75-80.[doi:10.13705/j.issn.1671-6833.2018.01.020]
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Journal of Zhengzhou University (Engineering Science)[ISSN
1671-6833/CN
41-1339/T] Volume:
39
Number of periods:
2018 04
Page number:
75-80
Column:
Public date:
2018-07-22
- Title:
- An Adaptive Quality Improved Algorithm in Low Dose CT Images
- CLC:
- -
- Abstract:
- The low-does CT（LDCT）scanning is an effective way to reduce the X-ray radiation dose. However, quantum noise caused by the reduction of radiation dose leads to degradation of image quality. We proposed a quality improvement algorithm of low-dose CT images based on the shearlet transformation. Firstly, LDCT image was transformed using the Anscombe transform, and the quantum noise was transformed into noise which approx imately obeyed Gaussian distribution. Secondly, the transformed image is decomposed into low-frequency coefficient sub-bands and multi-directional high-frequency coefficient sub-bands based on shearlet transform. Then, for high-frequency coefficient sub-bands of the low signal noise ratio, a noise variance estimation method basedon the residual autocorrelation power (RAP) was proposed, which was combined with Bayesian maximumposterior probability method to obtain the more accurate non-noise high-frequency cofficients. Finally, the reconstructed image was obtained using the shearlet inverse transform and anscombe inverse transform. A series of experimental results of quantitative evakuation and visual effects showed that the proposed algorithm outperformed the de-noising method based on wavelet domain. The quality of the reconstructed image, compared with the denoising algorithm based on wavelet domain, the Peak Signal Noise Ratio(PSNR) was increased averagely by 52.2%,and the Mean Structure Similarity(MSSIM) was increased by 34.4%.
- References:
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Last Update:
2018-07-26
