Journal of Computing Science and Engineering

  • 제6권1호
  • /
  • Pages.1-11
  • /
  • 2012
  • /
  • 1976-4677(pISSN)
  • /
  • 2093-8020(eISSN)
한국정보과학회 (Korean Institute of Information Scientists and Engineers)
권호 표지
DOI QR코드

DOI QR Code

Fast and Accurate Rigid Registration of 3D CT Images by Combining Feature and Intensity

  • June, Naw Chit Too (School of Information and Communication Engineering, Inha University) ;
  • Cui, Xuenan (School of Information and Communication Engineering, Inha University) ;
  • Li, Shengzhe (School of Information and Communication Engineering, Inha University) ;
  • Kim, Hak-Il (School of Information and Communication Engineering, Inha University) ;
  • Kwack, Kyu-Sung (Department of Radiology, Ajou University School of Medicine)
  • 투고 : 2011.07.22
  • 심사 : 2012.01.29
  • 발행 : 2012.03.30
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Computed tomography (CT) images are widely used for the analysis of the temporal evaluation or monitoring of the progression of a disease. The follow-up examinations of CT scan images of the same patient require a 3D registration technique. In this paper, an automatic and robust registration is proposed for the rigid registration of 3D CT images. The proposed method involves two steps. Firstly, the two CT volumes are aligned based on their principal axes, and then, the alignment from the previous step is refined by the optimization of the similarity score of the image's voxel. Normalized cross correlation (NCC) is used as a similarity metric and a downhill simplex method is employed to find out the optimal score. The performance of the algorithm is evaluated on phantom images and knee synthetic CT images. By the extraction of the initial transformation parameters with principal axis of the binary volumes, the searching space to find out the parameters is reduced in the optimization step. Thus, the overall registration time is algorithmically decreased without the deterioration of the accuracy. The preliminary experimental results of the study demonstrate that the proposed method can be applied to rigid registration problems of real patient images.

키워드

참고문헌

  1. B. Zitova and J. Flusser, "Image registration methods: a survey," Image and Vision Computing, vol. 21, no. 11, pp. 977- 1000, 2003. https://doi.org/10.1016/S0262-8856(03)00137-9
  2. J. B. A. Maintz and M. A. Viergever, "A survey of medical image registration," Medical Image Analysis, vol. 2, no. 1, pp. 1-36, 1998. https://doi.org/10.1016/S1361-8415(01)80026-8
  3. V. Walimbe and R. Shekhar, "Automatic elastic image registration by interpolation of 3D rotations and translations from discrete rigid-body transformations," Medical Image Analysis, vol. 10, no. 6, pp. 899-914, 2006. https://doi.org/10.1016/j.media.2006.09.002
  4. T. Rhee, J. P. Lewis, K. Nayak, and U. Neumann, "Adaptive non-rigid registration of 3D knee MRI in different pose spaces," in 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Paris, France, 2008. pp. 1111-1114.
  5. N. M. Alpert, J. F. Bradshaw, D. Kennedy, and J. A. Correia, "The principal axes transformation - a method for image registration," Journal of Nuclear Medicine, vol. 31, no. 10, pp. 1717, 1990.
  6. L. K. Arata, A. P. Dhawan, J. P. Broderick, M. F. Gaskil-Shipley, A. V. Levy, and N. D. Volkow, "Three-dimensional anatomical model-based segmentation of MR brain images through principal axes registration," IEEE Transactions on Biomedical Engineering, vol. 42, no. 11, pp. 1069-1078, 1995. https://doi.org/10.1109/10.469373
  7. H. Bulow, L. Dooley, and D. Wermser, "Application of principal axes for registration of NMR image sequences," Pattern Recognition Letters, vol. 21, no. 4, pp. 329-336, 2000. https://doi.org/10.1016/S0167-8655(99)00163-4
  8. L. Shang, L. J. Cheng, and Z. Yi, "Rigid medical image registration using PCA neural network," Neurocomputing, vol. 69, no. 13-15, pp. 1717-1722, 2006. https://doi.org/10.1016/j.neucom.2006.01.007
  9. D. L. Hill, C. Studholme, and D. J. Hawkes, "Voxel similarity measures for automated image registration," Visualization in Biomedical Computing. Bellingham, WA: SPIE, 1994, pp. 205.
  10. Y. Zhang, J. C. H. Chu, W. Hsi, A. J. Khan, P. S. Mehta, D. B. Bernard, and R. A. Abrams, "Evaluation of four volumebased image registration algorithms," Medical Dosimetry, vol. 34, no. 4, pp. 317-322, 2009. https://doi.org/10.1016/j.meddos.2008.12.004
  11. M. Holden, D. L. G. Hill, E. R. E. Denton, J. M. Jarosz, T. C. S. Cox, and D. J. Hawkes, "Voxel similarity measures for 3D serial MR brain image registration," Information Processing in Medical Imaging. Lecture Notes in Computer Science, vol. 1613, Heidelberg: Springer Verlag, pp. 472-477, 1999.
  12. W. M. Wells 3rd, P. Viola, H. Atsumi, S. Nakajima, and R. Kikinis, "Multi-modal volume registration by maximization of mutual information," Medical Image Analysis, vol. 1, no. 1, pp. 35-51, 1996. https://doi.org/10.1016/S1361-8415(01)80004-9
  13. Z. F. Knops, J. B. A. Maintz, M. A. Viergever, and J. P. W. Pluim, "Normalized mutual information based registration using k-means clustering and shading correction," Medical Image Analysis, vol. 10, no. 3, pp. 432-439, 2006. https://doi.org/10.1016/j.media.2005.03.009
  14. L. Ding, A. Goshtasby, and M. Satter, "Volume image registration by template matching," Image and Vision Computing, vol. 19, no. 12, pp. 821-832, 2001. https://doi.org/10.1016/S0262-8856(00)00101-3
  15. F. Zhao, Q. Huang, and W. Gao, "Image matching by normalized cross-correlation," Acoustics, Speech and Signal Processing, vol. 2, 2006.
  16. E. Schreibmann, and L. Xing, "Image registration with automapped control volumes," Medical Physics, vol. 33, pp. 1165, 2006. https://doi.org/10.1118/1.2184440
  17. F. Maes, D. Vandermeulen, and P. Suetens, "Comparative evaluation of multiresolution optimization strategies for multimodality image registration by maximization of mutual information," Medical Image Analysis, vol. 3, no. 4, pp. 373- 386, 1999. https://doi.org/10.1016/S1361-8415(99)80030-9
  18. Y. Yim, M. Wakid, C. Kirmizibayrak, S. Bielamowicz, and J. Han, "Registration of 3D CT data to 2D endoscopic image using a gradient mutual information based viewpoint matching for image-guided medialization laryngoplasty," Journal of Computing Science and Engineering, vol. 4, no. 4, pp. 368-387, 2010. https://doi.org/10.5626/JCSE.2010.4.4.368
  19. P. Thevenaz and M. Unser, "Optimization of mutual information for multiresolution image registration," IEEE Transactions on Image Processing, vol. 9, no. 12, pp. 2083-2099, 2000. https://doi.org/10.1109/83.887976
  20. M. Takao, N. Sugano, T. Nishii, H. Miki, T. Koyama, J. Masumoto, Y. Sato, S. Tamura, and H. Yoshikawa, "Application of 3D-MR image registration to monitor diseases around the knee joint," Journal of Magnetic Resonance Imaging, vol. 22, no. 5, pp. 656-660, 2005. https://doi.org/10.1002/jmri.20435
  21. M. Takao, N. Sugano, T. Nishii, H. Tanaka, J. Masumoto, H. Miki, Y. Sato, S. Tamura, and H. Yoshikawa, "Application of three-dimensional magnetic resonance image registration for monitoring hip joint diseases," Magnetic Resonance Imaging, vol. 23, no. 5, pp. 665-670, 2005. https://doi.org/10.1016/j.mri.2005.02.002
  22. S. Klein, M. Staring, K. Murphy, M. A. Viergever, and J. P. W. Pluim, "elastix: a toolbox for intensity-based medical image registration," IEEE Transactions on Medical Imaging, vol. 29, no. 1, pp. 196-205, 2010. https://doi.org/10.1109/TMI.2009.2035616
  23. D. Stein, K. H. Fritzsche, M. Nolden, H. P. Meinzer, and I. Wolf, "The extensible open-source rigid and affine image registration module of the Medical Imaging Interaction Toolkit (MITK)," Computer Methods and Programs in Biomedicine, vol. 100, no. 1, pp. 79-86, 2010. https://doi.org/10.1016/j.cmpb.2010.02.008
  24. Z. Lu, Q. Feng, P. Shi, and W. Chen, "A fast 3-D medical image registration algorithm based on equivalent meridian plane," IEEE International Conference on Image Processing, San Antonio, TX, 2007.
  25. Z. Lu, M. Zhang, Q. Feng, P. Shi, and W. Chen, "Medical image registration based on equivalent meridian plane," 4th International Conference on Image Analysis and Recognition. Lecture Notes in Computer Science, vol. 4633, Heidelberg: Springer Verlag, pp. 982-992, 2007.
  26. J. C. Lagarias, J. A. Reeds, M. H. Wright, and P. E. Wright, "Convergence properties of the Nelder-Mead simplex method in low dimensions," SIAM Journal on Optimization, vol. 9, no. 1, pp. 112-147, 1999. https://doi.org/10.1137/S1052623496303470
  27. N. Otsu, "A threshold selection method from gray-level histograms," IEEE Transactions on Systems, Man and Cybernetics, vol. 9, pp. 62-66, 1979. https://doi.org/10.1109/TSMC.1979.4310076

피인용 문헌

  1. Analysis of iodinated contrast delivered during thermal ablation: is material trapped in the ablation zone? vol.61, pp.16, 2016, https://doi.org/10.1088/0031-9155/61/16/6041
  2. Optimization for link quality and power consumption of visible light communication system vol.27, pp.3, 2014, https://doi.org/10.1007/s11107-014-0430-x
  3. The accuracy of a designed software for automated localization of craniofacial landmarks on CBCT images vol.14, pp.1, 2014, https://doi.org/10.1186/1471-2342-14-32
  4. Ablation zone visualization enhancement by periodic contrast-enhancement computed tomography during microwave ablation vol.44, pp.6, 2017, https://doi.org/10.1002/mp.12266