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.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1079394Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1637
 J. V. Hajnal, et al., "Medical image registration." Boca Raton: CRC Press, 2001.
 A. Goshtasby, "2-D and 3-D image registration for medical, remote sensing, and industrial applications." Hoboken, NJ: J. Wiley & Sons, 2005.
 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.
 E. Meijering, et al., "Tracking in molecular bioimaging," IEEE Signal Processing Magazine, 23(3), pp. 46-53, 2006.
 B. Zitova and J. Flusser, "Image registration methods: A survey," Image and Vision Computing, 21(11), pp. 977-1000, 2003.
 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.
 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.
 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.
 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.
 A. Rangarajan, et al., "Rigid point feature registration using mutual information," Medical Image Analysis, 3(4), pp. 425-440, 1999.
 M. N. Do and M. Vetterli, ""Contourlets," in Beyond Wavelets," Academic Press, New York, 2003.
 P. Burt and E. Adelson, "The Laplacian pyramid as a compact image code," IEEE Transactions on Communication, 31(4), pp. 532-540, 1983.
 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.
 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.
 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.
 A. Gholipour, et al., "Brain functional localization: A survey of image registration techniques," IEEE Transactions on Medical Imaging, 26(4), pp. 427-451, 2007.
 F. Maes, et al., "Multimodality image registration by maximization of mutual information," IEEE Transactions on Medical Imaging, 16(2), pp. 187-198, 1997.
 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.
 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.
 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.
 L. G. Brown, "A survey of image registration techniques," ACM Computing Surveys, 24(4), pp. 325-376, 1992.
 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.