X-ray image enhancement based on the dyadic wavelet transform

Journal of X-Ray Science and Technology

Volumn 14, Issue 2, 2006, Pages 83-93

  Qi, Zhihua ^a   Zhang, Li ^a   Xing, Yuxiang ^a   Li, Shuanglei ^b  

^a TSINGHUA UNIVERSITY   (China)

^b University of Illinois at Urbana Champaign   (United States)

Author keywords

Coefficient classification; Contrast enhancement; Denoising; Dyadic wavelet transform; Non linear mapping; Scatter reduction

Indexed keywords

ACOUSTIC NOISE; ALGORITHMS; ATTENUATION; DATA PROCESSING; IMAGE QUALITY; IMAGE RECONSTRUCTION; MAPPING; NONLINEAR SYSTEMS; SCATTERING; SIGNAL PROCESSING; TOMOGRAPHY; WAVELET TRANSFORMS; X RAY ANALYSIS;

COEFFICIENT CLASSIFICATION; CONTRAST ENHANCEMENT; DENOISING; DYADIC WAVELET TRANSFORM; NON-LINEAR MAPPING; SCATTER REDUCTION;

IMAGE ENHANCEMENT;

EID: 33745172460     PISSN: 08953996     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (16)

1. H.R. Blackwell, Contrast threshold of the human eye, J. Optical Soc. America 36 624-634.
2. J. Hsieh, Computed Tomography, Washington: SPIE Press, 2003.
3. M. Stahl, T. Aach and S. Dippel, Digital radiography enhancement by nonlinear wavelet processing, Med. Phy. 27(1) 56-65.
4. S. Dippel, M. Stahl, R. Wiemker and T. Blaffert, Multiscale contrast enhancement for radiographies: laplacian pyramid versus fast wavelet transform, IEEE Trans. Med. Imaging 21(4) (2002), 343-353.
5. A. Laine, J. Fan and W. Yang, Wavelets for contrast enhancement of digital mammography, IEEE Engineering in Medicine and Biology 14(5) (1995), 536-550.
6. A. Laine, S. Schuler, J. Fan and W. Huda, Mammographic feature enhancement by multiscale analysis, IEEE Trans. Med. Imaging 13(4) (1994), 725-740.
7. M. Stahl, T. Aach, T.M. Buzug, S. Dippel and U. Neitzel, Noise-resistant weak-structure enhancement for digital radiography, In Proc. SPIE. Image Processing 3661 (1999), 1406-1417.
8. C. Fivez, P. Vuylsteke, P. Wambacq, P. Suetens and E. Schoeters, Multi-resolution contrast amplification in digital radiography with compensation for scattered radiation, in Proc. IEEE Image Processing 1 (1996), 339-342.
9. S. Mallat and S. Zhong, Characterization of signals from multiscale edges, IEEE Trans. Pattern Anal. Machine Intell. 14 (1992), 710-732.
10. S. Mallat, A wavelet tour of signal processing, San Diego: Academic Press, 1998.
11. J. Shao, X-ray imaging degradation model and restoration algorithm, Ph. D. dissertation, Department of Mechanical Engineering, Beihang University, Beijing, 2005.
12. J. Zhong, R. Ning and D. Conover, Image denoising based on multiscale singularity detection for cone beam CT breast imaging, IEEE Trans. Medical Imaging 23(5) (2004), 696-703.
13. Z. Cai, T.H. Cheng, C. Lu and K.R. Subramanian, Efficient wavelet-based image denoising algorithm, Electron. Lett. 37(11) (May 2001), 683-685.
14. M.K. Michak, I. Kozintsev, K. Ramchandran and P. Moulin, Low-complexity image denoising based on statistical modeling of wavelet coefficients, IEEE Signal Processing Lett. (1999), 6.
15. L. Donoho and I.M. Johnstone, Ideal spatial adaptation by wavelet shrinkage, Biometrika 81(3) (1994), 425-455.
16. K.A. Feldamp, L.C. Davis and W. Kress, Practical cone-beam algorithm, J. Opt. Soc. Amer. 1(6) (1984), 612-619.