Email this article 
Open Access
Subscription or Fee Access
Monomodal Brain Image Registration Using Fast Walsh Hadamard Transform
Abstract
Image registration is of great significance to medicine and remote sensing, so a lot of techniques have been developed within the context of one or the other discipline. This paper suggests a method for medical image registration using Fast Walsh Hadamard transform. This algorithm can be used to register images of the same or different modalities using CT or/and MRI images. Each image bit is lengthened in terms of Fast Walsh Hadamard basis functions. Each basis function is a notion of determining various aspects of local structure, e.g., horizontal edge, corner, etc. These coefficients are normalized and used as numerals in a chosen number system which allows one to form a unique number for each type of local structure.Performance evaluation is done based on the indexes Mutual Information, Correlation Coefficient and the Elapsed time for registration. The experimental outcomes show that Fast Walsh Hadamard transform achieved good results than the conventional Walsh transform in the time domain. In addition Fast Walsh Hadamard transform carried out medical image registration consuming less time.
Keywords
References
D. Sasikala and R. Neelaveni, “Medical Image Registration Using Fast Walsh Hadamard Transform”, International Conference on Intelligent Information Systems and Management (IISM’2010), June10-12, 2010.
D. Sasikala and R. Neelaveni, “ Registration of Brain Images using Fast Walsh Hadamard Transform”, International Journal of Computer Science and Information Security Publication May 2010, Volume 8 No.2,pp. 96-105.
George K. Matsopoulos, Nicolaos A. Mouravliansky, Konstantinos K.Delibasis, and Konstantina S. Nikita, “Automatic Retinal Image Registration Scheme Using Global Optimization Techniques,” IEEE Transactions on Information Technology in Biomedicine, vol. 3, no. 1,pp. 47-60, 1999.
G. Wolberg, and S. Zokai, “Robust image registration using log-polar transform,” Proceedings of International Conference on Image Processing, vol. 1, pp. 493-496, 2000.
Yang-Ming Zhu, “Volume Image Registration by Cross-Entropy Optimization,” IEEE Transactions on Medical Imaging, vol. 21, no. 2,pp. 174-180, 2002.
Jan Kybic, and Michael Unser, “Fast Parametric Elastic Image Registration,” IEEE Transactions on Image Processing, vol. 12, no. 11,pp. 1427-1442, 2003.
Y. Bentoutou, N. Taleb, K. Kpalma, and J. Ronsin, “An Automatic Image Registration for Applications in Remote Sensing,” IEEE Transactions on Geosciences and Remote Sensing, vol. 43, no. 9, pp.2127-2137, 2005.
Luciano Silva, Olga R. P. Bellon, and Kim L. Boyer, “Precision Range Image Registration Using a Robust Surface Interpenetration Measure and Enhanced Genetic Algorithms,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 762-776, 2005.
R. Matungka, Y. F. Zheng, and R. L. Ewing, “Image registration using Adaptive Polar Transform,” 15th IEEE International Conference on Image Processing, ICIP 2008, pp. 2416-2419, 2008.
G. Khaissidi, H. Tairi and A. Aarab, “A fast medical image registration using feature points,” ICGST-GVIP Journal, vol. 9, no. 3, 2009.
Wei Pan, Kaihuai Qin, and Yao Chen, “An Adaptable-Multilayer Fractional Fourier Transform Approach for Image Registration,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no.3, pp. 400-413, 2009.
R. Matungka, Y. F. Zheng, and R. L. Ewing, “Image registration using Adaptive Polar Transform,” IEEE Transactions on Image Processing,vol. 18, no. 10, pp. 2340-2354, 2009.
Jr. Dennis M. Healy, and Gustavo K. Rohde, “Fast Global Image Registration using Random Projections,” 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2007,pp. 476-479, 2007.
Fookes and A. Maeder, “Quadrature-Based Image Registration Method using Mutual Information,” IEEE International Symposium on Biomedical Imaging: Nano to Macro, vol. 1, pp. 728-731, 2004.
M. Petrou and P. Bosdogianni, Image Processing—The Fundamentals.New York: Wiley, 1999.
Pere Marti-Puig, “A Family of Fast Walsh Hadamard Algorithms With Identical Sparse Matrix Factorization,” IEEE Transactions on Signal Processing Letters, vol. 13, no. 11, pp. 672-675, 2006.
J. L. Moigne, W. J. Campbell, and R. F. Cromp, “An automated parallel image registration technique based on correlation of wavelet features,”IEEE Trans. Geosci. Remote Sens., vol. 40, no. 8, pp. 1849–1864, Aug.2002.
J. P. W. Pluim, J. A. Maintz, and M. A. Viergever, “Image registration by maximization of combined mutual information and gradient information,” IEEE Trans. Med. Imag., vol. 19, no. 8, pp. 899–814, Aug.2000.
Z. Zhang, J. Zhang, M. Liao, and L. Zhang, “Automatic registration of multi-source imagery based on global image matching,” Photogramm.Eng. Remote Sens., vol. 66, no. 5, pp. 625–629, May 2000.
M. Bossert, E. M. Gabidulin, and P. Lusina, “Space-time codes based on Hadamard matrices proceedings,” in Proc. IEEE Int. Symp. Information Theory, Jun. 25–30, 2000, p. 283.
L. Ping, W. K. Leung, and K. Y. Wu, “Low-rate turbo-Hadamard codes,” IEEE Trans. Inf. Theory, vol. 49, no. 12, pp. 3213–3224, Dec.2003.
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.