Acknowledgement
This research was supported by the Korea Medical Device Development Fund grant funded by the Korea government (the Ministry of Science and ICT, the Ministry of Trade, Industry and Energy, the Ministry of Health & Welfare, the Ministry of Food and Drug Safety) (Project Number: 202011B26).
References
- H. Chan, K. Doi, Investigation of the performance of antiscatter grids: Monte Carlo simulation studies, Phys. Med. Biol. 27 (6) (1982) 785-803. https://doi.org/10.1088/0031-9155/27/6/002
- C. Lin, W. Lee, S. Chen, C. Tsai, J. Lee, C. Chang, Y. Ching, A study of grid artifacts formation and elimination in computed radiographic images, J. Digit. Imag. 19 (4) (2006) 351-361. https://doi.org/10.1007/s10278-006-0630-8
- D. Kim, S. Lee, Grid artifact reduction in radiography with arctan(1/2)-degree rotated grid, in: Proceedings of the IEEE International Conference on Image Processing, 2010, https://doi.org/10.1109/ICIP.2010.5653163. Hong Kong, China.
- J. Wang, H. Huang, Film digitization aliasing artifacts caused by grid line patterns, IEEE Trans. Med. Imag. 13 (2) (1994) 375-385. https://doi.org/10.1109/42.293930
- D. Gauntt, G. Barnes, Grid line artifact formation: a comprehensive theory, Med. Phys. 33 (6) (2006) 1668-1677. https://doi.org/10.1118/1.2164069
- D. Bednarek, S. Rudin, R. Wong, Artifacts produced by moving grids, Radiol. 147 (1) (1983) 255-258. https://doi.org/10.1148/radiology.147.1.6828740
- L. Barski, X. Wang, Characterization, detection, and suppression of stationary grids in digital projection radiography imagery, in: Proc. SPIE 3658, Medical Imaging 1999, Image Display, San Diego, CA, USA, 1999, https://doi.org/10.1117/12.349462.
- I. Belykh and C. Cornelius, Method for antiscatter stationary grid artifacts detection and attenuation in digital radiographic images, US Patent No. US 7050618B2 (23 May 2006).
- R. Sasada, M. Yamada, S. Hara, H. Takeo, K. Shimura, Stationary grid pattern removal using 2-dimensional technique for moire-free radiographic image display, in: Proc. SPIE 5029, Medical Imaging 2003: Visualization, Image-Guided Procedures, and Display, 2003, https://doi.org/10.1117/12.479595. San Diego, CA, USA.
- D. Gauntt, G. Barnes, A novel technique to suppress grid line artifacts, Med. Phys. 33 (6) (2006) 1654-1667. https://doi.org/10.1118/1.2184444
- J. Yoon, Y. Park, C. Park, D. Kim, J. Lee, N. Chung, B. Choe, T. Suh, H. Lee, Reduction of a grid moire pattern by integrating a carbon-interspaced high precision x-ray grid with a digital radiographic detector, Med. Phys. 34 (11) (2007) 4092-4097. https://doi.org/10.1118/1.2775743
- D. Kim, S. Lee, Grid artifact reduction for direct digital radiography detectors based on rotated stationary grids with homomorphic filtering, Med. Phys. 40 (6) (2013), 061905.
- R. Wilks, Principles of Radiological Physics, Churchill Livingston, New York, Edinburgh, 1981.
- T. Maruyama, H. Yamamoto, Elimination of Gridlines by Using Non-linear Filter in Mammographic Image, IST 2009 International Workshop on Imaging Systems and Techniques, 2009, https://doi.org/10.1049/iet-ipr.2009.0240. Shenzhen, China.
- I. Belykh, C. Cornelius, Antiscatter stationary grid artifacts automated detection and removal in projection radiography images, in: Proc. SPIE 4322, Medical Imaging 2001, Image Processing, San Diego, CA, USA, 2001, https://doi.org/10.1117/12.430992.
- G. Hudhud, M. Turner, Digital removal of power frequency artifacts using a fourier space median filter, IEEE Signal Process. Lett. 12 (8) (2005) 573-576. https://doi.org/10.1109/LSP.2005.851257
- A. Konstantinidis, A. Olivo, P. Munro, S. Bohndiek, R. Speller, Optical characterization of a CMOS active pixel sensor using periodic noise reduction techniques, Nucl. Instrum. Methods Phys. Res., Sect. A 620 (2) (2010) 549-556. https://doi.org/10.1016/j.nima.2010.03.138
- I. Aizenberg, C. Butakoff, A windowed Gaussian notch filter for quasi-periodic noise removal, Image Vis Comput. 26 (10) (2008) 1347-1353. https://doi.org/10.1016/j.imavis.2007.08.011
- D. Kim, S. Lee, Slope detector for determination of the ringing artifact in filtering x-ray images, in: Proceedings of the IEEE International Conference on Acoustics, Speech, Signal Processing, 2012, https://doi.org/10.1109/ICASSP.2012.6287983. Kyoto, Japan.
- J. Varghese, S. Subash, N. Tairan, Fourier transform-based windowed adaptive switching minimum filter for reducing periodic noise from digital images, IET Image Process. 10 (9) (2016) 646-656. https://doi.org/10.1049/iet-ipr.2015.0750
- P. Moallem, M. Masoumzadeh, M. Habibi, A novel adaptive Gaussian restoration filter for reducing periodic noises in digital image, Signal Image Video Process 9 (5) (2013) 1179-1191. https://doi.org/10.1007/s11760-013-0560-0
- J. Varghese, S. Subhash, K. Subramaniam, K. Sridhar, Adaptive Gaussian notch filter for removing periodic noise from digital images, IET Image Process. 14 (8) (2020) 1529-1538. https://doi.org/10.1049/iet-ipr.2018.5707
- H. Tang, D. Tong, X. Bao, J. Dillenseger, A new stationary gridline artifact suppression method based on the 2D discrete wavelet transform, Med. Phys. 42 (4) (2015) 1721-1729. https://doi.org/10.1118/1.4914861
- K. Shirai, S. Ono, M. Okuda, Frequency Spectrum Regularization for Pattern Noise Removal Based on Image Decomposition, 2017 25th European Signal Processing Conference (EUSIPCO), 2017, https://doi.org/10.23919/EUSIPCO.2017.8081465. Kos, Greece.
- F. Fang, T. Wang, S. Wu, G. Zhang, Removing moire patterns from single images, Inf. Sci. 14 (8) (2020) 1529-1538.
- K. Kim, H. Kim, H. Lee, J. Jung, J. Nam, J. Park, D. Kim, H. Kim, H. Kim, A grid-line suppression technique based on deep convolutional neural networks, in: Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 2020, 1131327, https://doi.org/10.1117/12.2549281. Houston, Texas, USA.
- A. Cunha, J. Zhou, M. Do, The nonsubsampled contourlet transform: theory, design, and applications, IEEE Trans. Image Process. 15 (10) (2006) 3089-3101. https://doi.org/10.1109/TIP.2006.877507
- S. Mallat, A theory for multiresolution signal decomposition: the wavelet representation, IEEE Trans. Pattern Anal. Mach. Intell. 11 (7) (1989) 674-693. https://doi.org/10.1109/34.192463
- S. Mallat, Multiresolution approximations and wavelet orthonormal bases of L2(R), Trans. Am. Math. Soc. 315 (1) (1989) 69-87. https://doi.org/10.1090/S0002-9947-1989-1008470-5
- F. Meskine, M. Mezouar, N. Taleb, A rigid image registration based on the nonsubsampled contourlet transform and genetic algorithms, Sensors-Basel 10 (9) (2010) 8553-8571. https://doi.org/10.3390/s100908553
- M. Do, M. Vetterli, The contourlet transform: an efficient directional multiresolution image representation, IEEE Trans. Image Process. 14 (12) (1989) 2091-2106. https://doi.org/10.1109/TIP.2005.859376
- M. Shensa, The discrete wavelet transforms: wedding the a trous and mallat algorithms, IEEE Trans. Signal Process. 40 (10) (1992) 2464-2482. https://doi.org/10.1109/78.157290
- R. Bamberger, M. Smith, A filter bank for the directional decomposition of images: theory and design, IEEE Trans. Signal Process. 40 (4) (1992) 882-893. https://doi.org/10.1109/78.127960
- E. Kang, J. Min, J. Ye, A deep convolutional neural network using directional wavelets for low-dose X-ray CT reconstruction, Med. Phys. 44 (10) (2017) e360-e375. https://doi.org/10.1002/mp.12344
- S. Lee, M. Lee, M. Kang, Poisson-Gaussian noise analysis and estimation for low-dose x-ray images in the NSCT domain, Sensors-Basel 18 (4) (2018) 1019.
- A. Paeth, A fast algorithm for general raster rotation, in: Proceedings of Graphics Interface and Vision Interface vol. 86, 1986, https://doi.org/10.20380/GI1986.15. Vancouver, British Columbia, Canada.
- L. Rudin, S. Osher, E. Fatemi, Nonlinear total variation based noise removal algorithms, Physica D 60 (1-4) (1992) 259-268. https://doi.org/10.1016/0167-2789(92)90242-F
- N. Parikh, S. Boyd, Proximal algorithms, Found. Trends Optim. 1 (3) (2014) 127-239. https://doi.org/10.1561/2400000003
- F. Ulaby, F. Kouyate, B. Brisco, T. Williams, Textural information in SAR images, IEEE Geosci. Remote Sens. (1986) 235-245. GE-24.