Correlation Coefficient Measure of Mono and Multimodal Brain Image Registration using Fast Walsh Hadamard Transform

A bundle of image registration procedures have been built up with enormous implication for data analysis in medicine, astrophotography, satellite imaging and little other areas. An approach to the problem of mono and multimodality medical image registration is proposed, with a fundamental concept Correlation Coefficient, as a matching measure. It measures the statistical dependence or information redundancy between the image intensities of corresponding voxels in both images. Maximization of CC is a very broad and dominant norm. As no assumptions are made regarding the nature of this dependence and no limiting constraints are imposed on the image content of the modalities involved. The accuracy of the CC criterion is validated for rigid body registration of computed tomography (CT), and magnetic resonance (MR T1 and T2) images by comparison with the registration solution. Experimental results prove that subvoxel accuracy with the reference solution can be achieved completely automatically without any preprocessing steps that make this process ensemble for medical applications.

[1]  Michael Unser,et al.  Fast parametric elastic image registration , 2003, IEEE Trans. Image Process..

[2]  Max A. Viergever,et al.  Image registration by maximization of combined mutual information and gradient information , 2000, IEEE Transactions on Medical Imaging.

[3]  Bing Luo,et al.  Inhomogeneous illuminated images registration based on wavelet decomposition , 2009, 2009 International Conference on Wavelet Analysis and Pattern Recognition.

[4]  Han-Ku Cho,et al.  Impact of registration error of reticle on total overlay error budget , 2006 .

[5]  Anthony J. Maeder,et al.  Quadrature-based image registration method using mutual information , 2004, 2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).

[6]  Dennis M. Healy,et al.  FAST GLOBAL IMAGE REGISTRATION USING RANDOM PROJECTIONS , 2007, 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[7]  Yunhua Zhang,et al.  InSAR Image Registration Using Modified Correlation Coefficient Algorithm , 2006, 2006 7th International Symposium on Antennas, Propagation & EM Theory.

[8]  Yuan F. Zheng,et al.  Image Registration Using Adaptive Polar Transform , 2009, IEEE Transactions on Image Processing.

[9]  Jacqueline Le Moigne,et al.  An automated parallel image registration technique based on the correlation of wavelet features , 2013, IEEE Trans. Geosci. Remote. Sens..

[10]  Kidiyo Kpalma,et al.  An automatic image registration for applications in remote sensing , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Konstantina S. Nikita,et al.  Automatic retinal image registration scheme using global optimization techniques , 1999, IEEE Transactions on Information Technology in Biomedicine.

[12]  Li Ping,et al.  Low-rate turbo-Hadamard codes , 2001, IEEE Trans. Inf. Theory.

[13]  Costas S. Iliopoulos,et al.  IACSIT International Journal of Engineering and Technology , 2013 .

[14]  Maria Petrou,et al.  Image processing - the fundamentals , 1999 .

[15]  Pere Martí-Puig,et al.  A Family of Fast Walsh Hadamard Algorithms With Identical Sparse Matrix Factorization , 2006, IEEE Signal Processing Letters.

[16]  Martin Bossert,et al.  Space-time codes based on Hadamard matrices , 2000, 2000 IEEE International Symposium on Information Theory (Cat. No.00CH37060).

[17]  M. Singh,et al.  From Human MRI to Microscopy: Co-registration of Human Brain Images to Postmortem Histological Sections , 2006, 2006 IEEE Nuclear Science Symposium Conference Record.

[18]  Maria Petrou,et al.  Image registration using the Walsh transform , 2006, IEEE Transactions on Image Processing.

[19]  George Wolberg,et al.  Robust image registration using log-polar transform , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[20]  Izumi Ito,et al.  Extension of DCT sign phase correlation to subpixel registration , 2009, 2008 International Symposium on Intelligent Signal Processing and Communications Systems.

[21]  Baojun Zhao,et al.  A Multi-Stage Astronomical Images Registration Based on Nonsubsampled Contourlet Transform , 2009, 2009 2nd International Congress on Image and Signal Processing.

[22]  Jean-Yves Tourneret,et al.  Bivariate Gamma Distributions for Image Registration and Change Detection , 2007, IEEE Transactions on Image Processing.

[23]  Wei Pan,et al.  An Adaptable-Multilayer Fractional Fourier Transform Approach for Image Registration , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[24]  D.R. Sullivan,et al.  A Coarse Search Correlation Tracker for Image Registration , 1981, IEEE Transactions on Aerospace and Electronic Systems.

[25]  On search space and search data strategies in image registration , 2005, Proceedings of OCEANS 2005 MTS/IEEE.

[26]  Hamid Tairi,et al.  A fast medical image registration using feature points , 2009 .

[27]  Derek L. G. Hill,et al.  Quantification of small cerebral ventricular volume changes in treated growth hormone patients using nonrigid registration , 2002, IEEE Transactions on Medical Imaging.

[28]  Dhamodaran Sasikala,et al.  Registration of Brain Images using Fast Walsh Hadamard Transform , 2010, ArXiv.

[29]  Tarek El-Ghazawi,et al.  Parameterized hardware design on reconfigurable computers: An image registration case study , 2009, 2009 5th Southern Conference on Programmable Logic (SPL).

[30]  Jordi Inglada,et al.  On the possibility of automatic multisensor image registration , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Kim L. Boyer,et al.  Precision range image registration using a robust surface interpenetration measure and enhanced genetic algorithms , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Yang-Ming Zhu,et al.  Volume Image Registration by Cross-Entropy Optimization , 2002, IEEE Trans. Medical Imaging.

[34]  Zuxun Zhang,et al.  Automatic Registration of Multi-Source Imagery Based on Global Image Matching , 2000 .