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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 696212, 16 pages
Noise Estimation for Single-Slice Sinogram of Low-Dose X-Ray Computed Tomography Using Homogenous Patch
1Visual Computing and Virtual Reality Key Laboratory Of Sichuan Province, Sichuan Normal University, Chengdu 610101, China
2School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China
3School of Information Science & Technology, East China Normal University, no. 500, Dong-Chuan Road, Shanghai 200241, China
Received 29 June 2011; Accepted 21 July 2011
Academic Editor: Shengyong Chen
Copyright © 2012 Zhiwu Liao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- O. W. Linton, A. Fred, and F. A. Mettler, “National conference on dose reduction in CT, with an emphasis on pediatric patients,” American Journal of Roentgenology, vol. 181, no. 2, pp. 321–329, 2003.
- Y. Zhang, J. Zhang, and H. Lu, “Statistical sinogram smoothing for low-dose CT with segmentation-based adaptive filtering,” IEEE Transactions on Nuclear Science, vol. 57, no. 5, part 1, pp. 2587–2598, 2010.
- H. Lu, X. Li, I. T. Hsiao, and Z. Liang, “Analytical noise treatment for low-dose CT projection data by penalized weighted least-square smoothing in the K-L domain,” in Proceedings of the SPIE Medical Imaging, vol. 4682, pp. 146–152, San Diego, Calif, USA, 2002.
- O. Demirkaya, “Reduction of noise and image artifacts in computed tomography by nonlinear filtration of the projection images,” in Proceedings of the SPIE Medical Imaging, vol. 4322, no. 2, pp. 917–923, San Diego, Calif, USA, 2001.
- J. Wang, H. Lu, Z. Liang et al., “An experimental study on the noise properties of x-ray CT sinogram data in Radon space,” Physics in Medicine and Biology, vol. 53, no. 12, pp. 3327–3341, 2008.
- H. Lu, I.-T. Hsiao, X. Li, and Z. Liang, “Noise properties of low-dose CT projections and noise treatment by scale transformations,” in Proceedings of the IEEE Nuclear Science Symposium Conference Record, vol. 3, pp. 1662–1666, 2001.
- J. Hsieh, “Adaptive streak artifact reduction in computed tomography resulting from excessive x-ray photon noise,” Medical Physics, vol. 25, no. 11, pp. 2139–2147, 1998.
- K. Sauer and B. Liu, “Non-stationary filtering of transmission tomograms in high photon counting noise,” IEEE Transactions on Medical Imaging, vol. 10, no. 3, pp. 445–452, 1991.
- B. R. Whiting, “Signal statistics of x-ray computed tomography,” Proceedings of the SPIE Physics of Medical Imaging, vol. 4682, pp. 53–60, 2002.
- B. R. Whiting, P. Massoumzadeh, and O. A. Earl, “Properties of preprocessed sinogram data in x-ray computed tomography,” Medical Physics, vol. 33, no. 9, pp. 3290–3303, 2006.
- P. Gravel, G. Beaudoin, and J. A. De Guise, “A method for modeling noise in medical images,” IEEE Transactions on Medical Imaging, vol. 23, no. 10, pp. 1221–1232, 2004.
- I. A. Elbakri and J. A. Fessier, “Efficient and accurate likelihood for iterative image reconstruction in X-ray computed tomography,” Proceedings of SPIE Image Processing, vol. 5032, pp. 1839–1850, 2003.
- J. Xu and B. M. W. Tsui, “Electronic noise modeling in statistical iterative reconstruction,” IEEE Transactions on Image Processing, vol. 18, no. 6, pp. 1228–1238, 2009.
- J. R. Mayo, K. P. Whittall, A. N. Leung et al., “Simulated dose reduction in conventional chest CT: validation study,” Radiology, vol. 202, no. 2, pp. 453–457, 1997.
- M. Gies, W. A. Kalender, H. Wolf, and C. Suess, “Dose reduction in CT by anatomically adapted tube current modulation. I. Simulation studies,” Medical Physics, vol. 26, no. 11, pp. 2235–2247, 1999.
- S. I. Olsen, “Estimation of noise in images: an evaluation,” Graphical Models and Image Processing, vol. 55, no. 4, pp. 319–323, 1993.
- J. Immerkær, “Fast noise variance estimation,” Computer Vision and Image Understanding, vol. 64, no. 2, pp. 300–302, 1996.
- D. Donoho, “De-noising by soft-thresholding,” IEEE Transactions on Information Theory, vol. 41, no. 3, pp. 613–627, 1995.
- D. L. Donoho and I. M. Johnstone, “Ideal spatial adaptation by wavelet shrinkage,” Biometrika, vol. 81, no. 3, pp. 425–455, 1994.
- F. Russo, “A method for estimation and filtering of Gaussian noise in images,” IEEE Transactions on Instrumentation and Measurement, vol. 52, no. 4, pp. 1148–1154, 2003.
- A. Foi, M. Trimeche, V. Katkovnik, and K. Egiazarian, “Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data,” IEEE Transactions on Image Processing, vol. 17, no. 10, pp. 1737–1754, 2008.
- C. Liu, W. T. Freeman, R. Szeliski, and S. B. Kang, “Noise estimation from a single image,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '06), vol. 1, pp. 901–908, 2006.
- A. De Stefano, P. White, and W. Collis, “Training methods for image noise level estimation on wavelet components,” EURASIP Journal on Applied Signal Processing, no. 16, pp. 2400–2407, 2004.
- S. C. Tai and S. M. Yang, “A fast method For image noise estimation using laplacian operator and adaptive edge detection,” in Proceedings of the 3rd International Symposium on Communications, Control and Signal Processing (ISCCSP '08), vol. 12–14, pp. 1077–1081, St Julians, Malta, March 2008.
- B. Antoni, C. Bartomeu, and M. J. Michel, “Nonlocal image and movie denoising,” International Journal of Computer Vision, vol. 76, no. 2, pp. 123–139, 2008, Special Section: Selection of Papers for CVPR 2005, Guest Editors: Cordelia Schmid, Stefano Soatto and Carlo Tomasi.
- A. Buades, B. Coll, and J. M. Morel, “A review of image denoising algorithms, with a new one,” Multiscale Modeling & Simulation, vol. 4, no. 2, pp. 490–530, 2005.
- Z. Liao, S. Hu, and W. Chen, “Determining neighborhoods of image pixels automatically for adaptive image denoising using nonlinear time series analysis,” Mathematical Problems in Engineering, vol. 2010, Article ID 914564, 14 pages, 2010.
- K. Vladimir, F. Alessandro, E. Karen, and A. Jaakko, “From local kernel to nonlocal multiple-model image denoising,” International Journal of Computer Vision, vol. 86, no. 1, pp. 1–32, 2010.
- Y. Li, Y. Chen, W. Chen, L. Luo, and X. Yin, “Improving low-dose X-ray CT images by weighted intensity averaging over large-scale neighborhoods,” in Proceedings of the International Conference on Medical Image Analysis and Clinical Application (MIACA '10), pp. 1–4, 2010.
- Z. S. Kelm, D. Blezek, B. Bartholmai, and B. J. Erickson, “Optimizing non-local means for denoising low dose CT,” in Proceedings of the IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI '09), pp. 662–665, 2009.
- B. I. Yi-ming, M. A. Jian-hua, L. I. U. Nan, et al., “Anscombe transform and BM3D filtering based projection restoration for low-dose CT reconstruction,” Computer Engineering and Applications, vol. 46, no. 13, pp. 216–220, 2010.
- J. Ma, J. Huang, Y. Chen, et al., “Generalized Gibbs Prior based high quality low-dose X-CT reconstruction,” Computer Engineering and Applications, vol. 44, no. 16, pp. 4–6, 2008.
- S. Y. Chen, Hanyang Tong, and Carlo Cattani, “Markov models for image labeling,” Mathematical Problems in Engineering, vol. 2012, Article ID 814356, 18 pages, 2012.
- S. Y. Chen and Y. F. Li, “Determination of stripe edge blurring for depth sensing,” IEEE Sensors Journal, vol. 11, no. 2, pp. 389–390, 2011.
- S. Y. Chen and Q. Guan, “Parametric shape representation by a deformable NURBS model for cardiac functional measurements,” IEEE Transactions on Biomedical Engineering, vol. 58, no. 3, pp. 480–487, 2011.
- S. Y. Chen, J. Zhang, H. Zhang et al., “Myocardial motion analysis for determination of tei-index of human heart,” Sensors, vol. 10, no. 12, pp. 11428–11439, 2010.
- E. G. Bakhoum and C. Toma, “Specific mathematical aspects of dynamics generated by coherence functions,” Mathematical Problems in Engineering, vol. 2011, Article ID 436198, 10 pages, 2011.
- E. G. Bakhoum and C. Toma, “Dynamical aspects of macroscopic and quantum transitions due to coherence function and time series events,” Mathematical Problems in Engineering, vol. 2010, Article ID 428903, 13 pages, 2010.
- J. Vandemeulebroucke, S. Rit, J. Kybic, P. Clarysse, and D. Sarrut, “Spatiotemporal motion estimation for respiratory-correlated imaging of the lungs,” Medical Physics, vol. 38, no. 1, pp. 166–178, 2011.
- J. L. Ong and A. K. Seghouane, “From point to local neighborhood: polyp detection in CT colonography using geodesic ring neighborhoods,” IEEE Transactions on Image Processing, vol. 20, no. 4, pp. 1000–1010, 2011.
- J. C. Ross, S. J. E. Rail, K. G. Diaz, et al., “Automatic lung lobe segmentation using particles, thin plate splines, and maximum a posteriori estimation,” Medical Image Computing and Computer-Assisted Intervention, vol. 6363, part 3, pp. 163–171, 2010.
- T. Li, X. Li, J. Wang et al., “Nonlinear sinogram smoothing for low-dose X-ray CT,” IEEE Transactions on Nuclear Science, vol. 51, no. 5, pp. 2505–2513, 2004.