Efficient Feature-Based Registration for CT-M R Images Based on NSCT and PSO
Feature-based registration is an effective technique for clinical use, because it can greatly reduce computational costs. However, this technique, which estimates the transformation by using feature points extracted from two images, may cause misalignments. To handle with this limitation, we propose to extract the salient edges and extracted control points (CP) of medical images by using efficiency of multiresolution representation of data nonsubsampled contourlet transform (NSCT) that finds the best feature points. The MR images were first decomposed using the NSCT, and then Edge and CP were extracted from bandpass directional subband of NSCT coefficients and some proposed rules. After edge and CP extraction, mutual information was adopted for the registration of feature points and translation parameters are calculated by using particle swarm optimization (PSO). The experimental results showed that the proposed method produces totally accurate performance for registration medical CT-MR images.
[1] J. V. Hajnal, et al., "Medical image registration." Boca Raton: CRC
Press, 2001.
[2] A. Goshtasby, "2-D and 3-D image registration for medical, remote
sensing, and industrial applications." Hoboken, NJ: J. Wiley & Sons,
2005.
[3] S. Rusinkiewicz and M. Levoy, "Efficient variants of the ICP
algorithm," in Proceedings Third International Conference on 3-D
Digital Imaging and Modeling, Quebec City, 2001, pp. 145-152.
[4] E. Meijering, et al., "Tracking in molecular bioimaging," IEEE Signal
Processing Magazine, 23(3), pp. 46-53, 2006.
[5] B. Zitova and J. Flusser, "Image registration methods: A survey," Image
and Vision Computing, 21(11), pp. 977-1000, 2003.
[6] W. Anna, et al., "A novel medical image registration algorithm based on
PSO and wavelet transformation combined with 2v-SVM," in Second
International Conference on Innovative Computing, Information and
Control, ICICIC '07, Kumamoto, Japan 2007, pp. 584-588.
[7] A. Wang, et al., "A novel medical image registration algorithm based on
PSO and wavelet transformation combined with 2v-SVM," in 2nd
International Conference on Innovative Computing, Information and
Control, ICICIC 2007, Kumamoto, Japan, 2008, pp. 584-588.
[8] A. L. d. Cunha, et al., "The nonsubsampled contourlet transform: theory,
design, and applications," IEEE Transactions on Image Processing,
15(10), pp. 3089-3101, 2006.
[9] J. P. Zhou, et al., "Nonsubsampled contourlet transform: construction
and application in enhancement," in IEEE International Conference on
Image Processing, ICIP2005, Genoa, Italy, 1, 2005, pp. 469-472.
[10] A. Rangarajan, et al., "Rigid point feature registration using mutual
information," Medical Image Analysis, 3(4), pp. 425-440, 1999.
[11] M. N. Do and M. Vetterli, ""Contourlets," in Beyond Wavelets,"
Academic Press, New York, 2003.
[12] P. Burt and E. Adelson, "The Laplacian pyramid as a compact image
code," IEEE Transactions on Communication, 31(4), pp. 532-540, 1983.
[13] R. H. Bamberger and M. J. T. Smith, "A filter bank for the directional
decomposition of images: Theory and design," IEEE Transactions on
Signal Processing, 40(4), pp. 882-893, 1992.
[14] C. Serief, et al., "Robust feature points extraction for image registration
based on the nonsubsampled contourlet transform," AEU - International
Journal of Electronics and Communications, 63(2), pp. 148-152, 2009.
[15] J. P. W. Pluim, et al., "Mutual-information-based registration of medical
images: a survey," IEEE Transactions on Medical Imaging, 22(8), pp.
986-1004, 2003.
[16] A. Gholipour, et al., "Brain functional localization: A survey of image
registration techniques," IEEE Transactions on Medical Imaging, 26(4),
pp. 427-451, 2007.
[17] F. Maes, et al., "Multimodality image registration by maximization of
mutual information," IEEE Transactions on Medical Imaging, 16(2), pp.
187-198, 1997.
[18] J. Kennedy and R. C. Eberhart, "Particle swarm optimization," in
Proceedings of IEEE International Conference on Neural Networks,
Piscataway, NJ, 4, 1995, pp. 1942-1948.
[19] B. Birge, "PSOt - a particle swarm optimization toolbox for use with
Matlab," in Proceedings of the 2003 IEEE Swarm Intelligence
Symposium, 2003. SIS '03, Indianapolis (IN), USA, 2003, pp. 182-186.
[20] W. Benzheng, et al., "An improved PSO algorithm based multimodality
medical image automatic registration," in Fourth International
Conference on Natural Computation, ICNC '08, Jinan, 7, 2008, pp. 574-
578.
[21] L. G. Brown, "A survey of image registration techniques," ACM
Computing Surveys, 24(4), pp. 325-376, 1992.
[22] A. E. R. Arce-Santana and A. Alba, "Image registration using Markov
random coefficient and geometric transformation fields " Pattern
Recognition 42(8), pp. 1660-1671, 2009.
[1] J. V. Hajnal, et al., "Medical image registration." Boca Raton: CRC
Press, 2001.
[2] A. Goshtasby, "2-D and 3-D image registration for medical, remote
sensing, and industrial applications." Hoboken, NJ: J. Wiley & Sons,
2005.
[3] S. Rusinkiewicz and M. Levoy, "Efficient variants of the ICP
algorithm," in Proceedings Third International Conference on 3-D
Digital Imaging and Modeling, Quebec City, 2001, pp. 145-152.
[4] E. Meijering, et al., "Tracking in molecular bioimaging," IEEE Signal
Processing Magazine, 23(3), pp. 46-53, 2006.
[5] B. Zitova and J. Flusser, "Image registration methods: A survey," Image
and Vision Computing, 21(11), pp. 977-1000, 2003.
[6] W. Anna, et al., "A novel medical image registration algorithm based on
PSO and wavelet transformation combined with 2v-SVM," in Second
International Conference on Innovative Computing, Information and
Control, ICICIC '07, Kumamoto, Japan 2007, pp. 584-588.
[7] A. Wang, et al., "A novel medical image registration algorithm based on
PSO and wavelet transformation combined with 2v-SVM," in 2nd
International Conference on Innovative Computing, Information and
Control, ICICIC 2007, Kumamoto, Japan, 2008, pp. 584-588.
[8] A. L. d. Cunha, et al., "The nonsubsampled contourlet transform: theory,
design, and applications," IEEE Transactions on Image Processing,
15(10), pp. 3089-3101, 2006.
[9] J. P. Zhou, et al., "Nonsubsampled contourlet transform: construction
and application in enhancement," in IEEE International Conference on
Image Processing, ICIP2005, Genoa, Italy, 1, 2005, pp. 469-472.
[10] A. Rangarajan, et al., "Rigid point feature registration using mutual
information," Medical Image Analysis, 3(4), pp. 425-440, 1999.
[11] M. N. Do and M. Vetterli, ""Contourlets," in Beyond Wavelets,"
Academic Press, New York, 2003.
[12] P. Burt and E. Adelson, "The Laplacian pyramid as a compact image
code," IEEE Transactions on Communication, 31(4), pp. 532-540, 1983.
[13] R. H. Bamberger and M. J. T. Smith, "A filter bank for the directional
decomposition of images: Theory and design," IEEE Transactions on
Signal Processing, 40(4), pp. 882-893, 1992.
[14] C. Serief, et al., "Robust feature points extraction for image registration
based on the nonsubsampled contourlet transform," AEU - International
Journal of Electronics and Communications, 63(2), pp. 148-152, 2009.
[15] J. P. W. Pluim, et al., "Mutual-information-based registration of medical
images: a survey," IEEE Transactions on Medical Imaging, 22(8), pp.
986-1004, 2003.
[16] A. Gholipour, et al., "Brain functional localization: A survey of image
registration techniques," IEEE Transactions on Medical Imaging, 26(4),
pp. 427-451, 2007.
[17] F. Maes, et al., "Multimodality image registration by maximization of
mutual information," IEEE Transactions on Medical Imaging, 16(2), pp.
187-198, 1997.
[18] J. Kennedy and R. C. Eberhart, "Particle swarm optimization," in
Proceedings of IEEE International Conference on Neural Networks,
Piscataway, NJ, 4, 1995, pp. 1942-1948.
[19] B. Birge, "PSOt - a particle swarm optimization toolbox for use with
Matlab," in Proceedings of the 2003 IEEE Swarm Intelligence
Symposium, 2003. SIS '03, Indianapolis (IN), USA, 2003, pp. 182-186.
[20] W. Benzheng, et al., "An improved PSO algorithm based multimodality
medical image automatic registration," in Fourth International
Conference on Natural Computation, ICNC '08, Jinan, 7, 2008, pp. 574-
578.
[21] L. G. Brown, "A survey of image registration techniques," ACM
Computing Surveys, 24(4), pp. 325-376, 1992.
[22] A. E. R. Arce-Santana and A. Alba, "Image registration using Markov
random coefficient and geometric transformation fields " Pattern
Recognition 42(8), pp. 1660-1671, 2009.
@article{"International Journal of Medical, Medicine and Health Sciences:61565", author = "Nemir Al-Azzawi and Harsa A. Mat Sakim and Wan Ahmed K. Wan Abdullah and Yasmin Mohd Yacob", title = "Efficient Feature-Based Registration for CT-M R Images Based on NSCT and PSO", abstract = "Feature-based registration is an effective technique for clinical use, because it can greatly reduce computational costs. However, this technique, which estimates the transformation by using feature points extracted from two images, may cause misalignments. To handle with this limitation, we propose to extract the salient edges and extracted control points (CP) of medical images by using efficiency of multiresolution representation of data nonsubsampled contourlet transform (NSCT) that finds the best feature points. The MR images were first decomposed using the NSCT, and then Edge and CP were extracted from bandpass directional subband of NSCT coefficients and some proposed rules. After edge and CP extraction, mutual information was adopted for the registration of feature points and translation parameters are calculated by using particle swarm optimization (PSO). The experimental results showed that the proposed method produces totally accurate performance for registration medical CT-MR images.
", keywords = "Feature-based registration, mutual information, nonsubsampled contourlet transform, particle swarm optimization.", volume = "5", number = "2", pages = "60-4", }
