Browse > Article
http://dx.doi.org/10.5626/JCSE.2012.6.1.1

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)
Publication Information
Journal of Computing Science and Engineering / v.6, no.1, 2012 , pp. 1-11 More about this Journal
Abstract
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.
Keywords
Image registration; Computed tomography; Principal axes; Normalized cross correlation;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 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.   DOI   ScienceOn
2 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.   DOI   ScienceOn
3 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.   DOI   ScienceOn
4 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.
5 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.
6 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.   DOI   ScienceOn
7 N. Otsu, "A threshold selection method from gray-level histograms," IEEE Transactions on Systems, Man and Cybernetics, vol. 9, pp. 62-66, 1979.   DOI   ScienceOn
8 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.   DOI   ScienceOn
9 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.   DOI   ScienceOn
10 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.   DOI   ScienceOn
11 F. Zhao, Q. Huang, and W. Gao, "Image matching by normalized cross-correlation," Acoustics, Speech and Signal Processing, vol. 2, 2006.
12 E. Schreibmann, and L. Xing, "Image registration with automapped control volumes," Medical Physics, vol. 33, pp. 1165, 2006.   DOI   ScienceOn
13 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.   DOI   ScienceOn
14 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.   DOI   ScienceOn
15 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.   DOI   ScienceOn
16 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.   DOI   ScienceOn
17 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.   DOI   ScienceOn
18 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.
19 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.
20 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.   DOI   ScienceOn
21 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.   DOI   ScienceOn
22 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.
23 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.   DOI   ScienceOn
24 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.
25 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.   DOI   ScienceOn
26 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.   DOI   ScienceOn
27 B. Zitova and J. Flusser, "Image registration methods: a survey," Image and Vision Computing, vol. 21, no. 11, pp. 977- 1000, 2003.   DOI   ScienceOn