A Denoising Method for Low-dose Small-animal Computed Tomography Image Based on Globe Dictionary Learning_Journal of Biomedical Engineering

Authors：

LIZhongyuan , LIGuang , SUNYi , CHENGong ,  LUOShouhua

School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China;

Corresponding author：

LUOShouhua, Email: luoshouhua@seu.edu.cn

Keywords：

dictionary learning; low-dose; small animal computed tomography; image denoising

DOI：

10.7507/1001-5515.20160048

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Considering the survival rate of small animals and the continuity of the experiments, high-dose X-ray shooting process is not suitable for the small animals in computed tomography (CT) experiments. But the low-dose process results with images might be polluted by noises which are not conducive for the experiments. In order to solve this problem, we in this paper introduce a global dictionary learning based denoising method to apply the promotion of the low dose CT image. We at first adopted the K-means singular value decomposition (K-SVD) algorithm to train a global dictionary based on the high dose CT image. Then, the noise image could be decomposed into sparse component which was free from noise through the orthogonal matching pursuit (OMP) algorithm. Finally, the noise-free image could be achieved by reconstructing the image only with its sparse components. The experiments results showed that the method we proposed here could decrease the noise efficiently and remain the details, and it would help promote the low dose image quality and increase the survival rate of the small animals.

Citation： LIZhongyuan, LIGuang, SUNYi, CHENGong, LUOShouhua. A Denoising Method for Low-dose Small-animal Computed Tomography Image Based on Globe Dictionary Learning. Journal of Biomedical Engineering, 2016, 33(2): 279-286. doi: 10.7507/1001-5515.20160048 Copy

1.	崔艳芬, 孙保存.小鼠乳腺肿瘤病毒及其与乳腺癌的关系[J].国际肿瘤学杂志, 2009, 36:127-130.
2.	WANG Jing, LI Tianfang, LU Hongbing, et al. Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray computed tomography[J]. IEEE Trans Med Imaging, 2006, 25(10):1272-1283.
3.	LI Tian-fang, XIANG Li, JING Wang, et al. Nonlinear sinogram smoothing for low-dose X-ray CT[J]. Nuclear Science, IEEE Transactions on, 2004, 51(5):2505-2513.
4.	ELBAKRI I A, FESSLER J A. Efficient and accurate likelihood for iterative image reconstruction in X-ray computed Tomography[C]//Proc SPIE, 2003:1839-1850.
5.	WANG Jing, LI Tianfang, XING Lei. Iterative image reconstruction for CBCT using edge-preserving prior[J]. Med Phys, 2009, 36(1):252-260.
6.	TAKEDA H, FARSIU S, MILANFAR P. Kernel regression for image processing and reconstruction[J]. IEEE Trans Image Process, 2007, 16(2):349-366.
7.	RUDIN L I, OSHER S, FATEMI E. Nonlinear total variation based noise removal algorithms[J]. Physica D, 1992, 60(1/4):259-268.
8.	BORSDORF A, RAUPACH R, FLOHR T, et al. Wavelet based noise reduction in CT-images using correlation analysis[J]. IEEE Trans Med Imaging, 2008, 27(12):1685-1703.
9.	MALLAT S G, ZHANG Z. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993, 41(12):3397-3415.
10.	CHEN S S, DONOHO D L, SAUNDERS M A. Atomic decomposition by basis pursuit[J]. SIAM Review, 2001, 43(1):129-159.
11.	DONOHO D L, HUO X. Uncertainty principles and ideal atomic decomposition[J]. IEEE Transactions on Information Theory, 2001, 47(7):2845-2862.
12.	AHARON M, ELAD M, BRUCKSTEIN A. K-SVD:an algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11):4311-4322.
13.	ELAD M, AHARON M. Image denoising via sparse and redundant representations over learned dictionaries[J]. IEEE Trans Image Process, 2006, 15(12):3736-3745.
14.	PATI Y C, REZAIIFAR R, KRISHNAPRASAD P S. Orthogonal matching pursuit:Recursive function approximation with applications to wavelet decomposition[C]//1993 Conference Record of The Twenty-Seventh Asilomar Conference on Signals, Systems and Computers, 1993. Pacific Grove, CA:1993, 1:40-44.
15.	WANG Zhou, BOVIK A C, SHEIKH H R, et al. Image quality assessment:from error visibility to structural similarity[J]. IEEE Trans Image Process, 2004, 13(4):600-612.
16.	Institute für radiologische Spezialdiagnostik, Northeast University, America[DB/OL].(2008-09-01)[2015-06-14].http://www.radiolog.at/start.php?bereich=ct_mr&nav=untersuchungen&lw=Computertomographie.
17.	ELBAKRI I A, FESSLER J A. Statistical image reconstruction for polyenergetic X-ray computed tomography[J]. IEEE Trans Med Imaging, 2002, 21(2):89-99.

1. 崔艳芬, 孙保存.小鼠乳腺肿瘤病毒及其与乳腺癌的关系[J].国际肿瘤学杂志, 2009, 36:127-130.
2. WANG Jing, LI Tianfang, LU Hongbing, et al. Penalized weighted least-squares approach to sinogram noise reduction and image reconstruction for low-dose X-ray computed tomography[J]. IEEE Trans Med Imaging, 2006, 25(10):1272-1283.
3. LI Tian-fang, XIANG Li, JING Wang, et al. Nonlinear sinogram smoothing for low-dose X-ray CT[J]. Nuclear Science, IEEE Transactions on, 2004, 51(5):2505-2513.
4. ELBAKRI I A, FESSLER J A. Efficient and accurate likelihood for iterative image reconstruction in X-ray computed Tomography[C]//Proc SPIE, 2003:1839-1850.
5. WANG Jing, LI Tianfang, XING Lei. Iterative image reconstruction for CBCT using edge-preserving prior[J]. Med Phys, 2009, 36(1):252-260.
6. TAKEDA H, FARSIU S, MILANFAR P. Kernel regression for image processing and reconstruction[J]. IEEE Trans Image Process, 2007, 16(2):349-366.
7. RUDIN L I, OSHER S, FATEMI E. Nonlinear total variation based noise removal algorithms[J]. Physica D, 1992, 60(1/4):259-268.
8. BORSDORF A, RAUPACH R, FLOHR T, et al. Wavelet based noise reduction in CT-images using correlation analysis[J]. IEEE Trans Med Imaging, 2008, 27(12):1685-1703.
9. MALLAT S G, ZHANG Z. Matching pursuits with time-frequency dictionaries[J]. IEEE Transactions on Signal Processing, 1993, 41(12):3397-3415.
10. CHEN S S, DONOHO D L, SAUNDERS M A. Atomic decomposition by basis pursuit[J]. SIAM Review, 2001, 43(1):129-159.
11. DONOHO D L, HUO X. Uncertainty principles and ideal atomic decomposition[J]. IEEE Transactions on Information Theory, 2001, 47(7):2845-2862.
12. AHARON M, ELAD M, BRUCKSTEIN A. K-SVD:an algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 2006, 54(11):4311-4322.
13. ELAD M, AHARON M. Image denoising via sparse and redundant representations over learned dictionaries[J]. IEEE Trans Image Process, 2006, 15(12):3736-3745.
14. PATI Y C, REZAIIFAR R, KRISHNAPRASAD P S. Orthogonal matching pursuit:Recursive function approximation with applications to wavelet decomposition[C]//1993 Conference Record of The Twenty-Seventh Asilomar Conference on Signals, Systems and Computers, 1993. Pacific Grove, CA:1993, 1:40-44.
15. WANG Zhou, BOVIK A C, SHEIKH H R, et al. Image quality assessment:from error visibility to structural similarity[J]. IEEE Trans Image Process, 2004, 13(4):600-612.
16. Institute für radiologische Spezialdiagnostik, Northeast University, America[DB/OL].(2008-09-01)[2015-06-14].http://www.radiolog.at/start.php?bereich=ct_mr&nav=untersuchungen&lw=Computertomographie.
17. ELBAKRI I A, FESSLER J A. Statistical image reconstruction for polyenergetic X-ray computed tomography[J]. IEEE Trans Med Imaging, 2002, 21(2):89-99.

Previous Article
Influence of Different Abduction Angles of Hip Joint on Stress Distribution of Femoral Neck
Next Article
Automated Tissue Characterization of Intravascular Ultrasound Gray-scale Images

Journal of Biomedical Engineering

A Denoising Method for Low-dose Small-animal Computed Tomography Image Based on Globe Dictionary Learning

Abstract Full text Figures/Tables Video References Cited by

Previous Article

Next Article

Format

Content