doi: 10.7763/IJET.2011.V3.215
Correlation Coefficient Measure of Mono and Multimodal Brain Image Registration using Fast Walsh Hadamard Transform
D. Sasikala
1
,
R. Neelaveni
2
- 1 Department of Computer Science and Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu 638401, India
- 2 Department of Electrical and Electronics Engineering, PSG College of Technology, Coimbatore, Tamil Nadu 641004, India
Abstract
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.
Keywords
- Image Registration
- Mono and Multimodal Brain Images
- Walsh Transform
- Fast Walsh Hadamard Transform
- Correlation Coefficient
How to Cite
D. Sasikala and R. Neelaveni, "Correlation Coefficient Measure of Mono and Multimodal Brain Image Registration using Fast Walsh Hadamard Transform," International Journal of Engineering and Technology, vol. 3, no. 2, pp. 154-160, 2011. https://doi.org/ 10.7763/IJET.2011.V3.215
Copyright & License
Copyright © 2011 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).