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http://dx.doi.org/10.9717/kmms.2012.15.12.1442

Metal Area Segmentation in X-ray CT Images Using the RNA (Relevant Neighbor Ar ea) Principle  

Kim, Youngshin (School of Electronic and Electrical Computer Engineering, Sungkyunkwan University)
Kwon, Hyukjoon (School of Electronic and Electrical Computer Engineering, Sungkyunkwan University)
Kim, Joongkyu (School of Electronic and Electrical Computer Engineering, Sungkyunkwan University)
Yi, Juneho (School of Electronic and Electrical Computer Engineering, Sungkyunkwan University)
Publication Information
Journal of Korea Multimedia Society / v.15, no.12, 2012 , pp. 1442-1448 More about this Journal
Abstract
The problem of Metal Area Segmentation (MAS) in X-ray CT images is a very hard task because of metal artifacts. This research features a practical yet effective method for MAS in X-ray CT images that exploits both projection image and reconstructed image spaces. We employ the Relevant Neighbor Area (RNA) idea [1] originally developed for projection image inpainting in order to create a novel feature in the projection image space that distinctively represents metal and near-metal pixels with opposite signs. In the reconstructed result of the feature image, application of a simple thresholding technique provides accurate segmentation of metal areas due to nice separation of near-metal areas from metal areas in its histogram.
Keywords
Medical Imaging; X-ray CT; Metal Area Segmentation; Relevant Neighbor Area; Metal Artifacts Reduction;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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1 H. Xue, Y. Xiao, Z. Chen, and Y. Xing, "Metal Artifact Reduction in Dual Energy CT by Sinogram Segmentation Based on Active Contour Model and TV Inpainting," IEEE Nuclear Science Symposium Conference Record, pp. 904-908, 2009.
2 J. Gu, L. Zhang, G. Yu, Y. Xing, and Z. Chen, "X-ray CT Metal Artifacts Reduction Through Curvature Based Sinogram Inpainting," Journal of X-Ray Science and Technology, Vol. 14, No. 2, pp. 73-82, 2006.
3 M. Oehler and T. M. Buzug, "The ${\lambda}$-MLEM Algorithm: An Iterative Reconstruction Technique for Metal Artifact Reduction in CT," Advances in Medical Engineering, Vol. 114, No. 1, pp. 42-47, 2007.   DOI
4 H. Akhoondali, R. A. Zoroofi, and G. Shirani, "Rapid Automatic Segmentation and Visualization of Teeth in CT-scan Data," Journal of Applied Sciences, Vol. 9, No. 11, pp. 2031-2044, 2009.   DOI
5 Y. Zhang et al, "Reducing Metal Artifacts in Cone-beam CT Images by Preprocessing Projection Data," International Journal of Radiation Oncology, Biology, Vol. 67, No. 3, pp. 924-32, 2007.   DOI
6 J. H. Hubbell and S. M. Seltzer, "Tables of X-Ray Mass Attenuation Coefficients from 1 keV to 20 MeV for Elements Z = 1 to 92 and 48 Additional Substances of Dosimetric Interest and Mass Energy-Absorption Coefficients. Available," http://physics.nist.gov/PhysRefData/XrayMassCoef/cover.html, 1996.
7 Y. Kim, S. Yoon, and J. Yi, "Effective Sinogram-inpainting for Metal Artifacts Reduction in X-ray CT Images," IEEE International Conference on Image Processing, pp. 597-600, 2010.
8 N. Otsu, "A Threshold Selection Method from Gray-level Histograms," IEEE Transactions on Systems, Man, and Cybernetics, Vol. 9, No. 1, pp. 62-66, 1979.   DOI   ScienceOn
9 G. Jang, H. Park, S. Lee, D. Kim, and M. Im, "An Effective Extraction Algorithm of Pulmonary Regions Using Intensity-level Maps in Chest X-ray Images," Journal of Korea Multimedia Society, Vol. 13, No. 7, pp. 1062-1075, 2010.   과학기술학회마을
10 Wouter J. H. Veldkamp, Raoul M. S. Joemai, Aart J. van der Molen, and Geleijns J., "Development and Validation of Segmentation and Interpolation Techniques in Sinograms for Metal Artifact Suppression in CT," Medical Physics, Vol. 37, No. 2, pp. 620-628, 2010.   DOI