Robust Image Registration Based on an Adaptive Normalized Mutual Information Metric

Authors: Huda Algharib, Amal Algharib, Hanan Algharib, Ali Mohammad Alqudah

Abstract:

Image registration is an important topic for many imaging systems and computer vision applications. The standard image registration techniques such as Mutual information/ Normalized mutual information -based methods have a limited performance because they do not consider the spatial information or the relationships between the neighbouring pixels or voxels. In addition, the amount of image noise may significantly affect the registration accuracy. Therefore, this paper proposes an efficient method that explicitly considers the relationships between the adjacent pixels, where the gradient information of the reference and scene images is extracted first, and then the cosine similarity of the extracted gradient information is computed and used to improve the accuracy of the standard normalized mutual information measure. Our experimental results on different data types (i.e. CT, MRI and thermal images) show that the proposed method outperforms a number of image registration techniques in terms of the accuracy.

Keywords: Image registration, mutual information, image gradients, Image transformations.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1474936

References:

[1] B. Zitová and J. Flusser, “Image registration methods: A survey,” Image Vis. Comput., vol. 21, pp. 977–1000, 2003.
[2] W. M. Wells III, P. Viola, H. Atsumi, S. Nakajima, and R. Kikinis, “Multimodal volume registration by maximization of mutual information,” Medical Image Analysis, vol. 1, no. 1, pp. 35–51, 1996.
[3] F. Maes, A. Collignon, D. Vandermeulen, G. Marchal, and P. Suetens, “Multimodality image registration by maximization of mutual information,” IEEE Transactions on Medical Imaging, vol. 16, no. 2, pp. 187–198, 1997.
[4] C. R. Meyer, J. L. Boes, B. Kim, P. H. Bland, K. R. Zasadny, P. V. Kison, K. Koral, K. A. Frey, and R. L. Wahl, “Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations,”Medical Image Analysis, vol. 1, no. 3, pp. 195–206, 1997.
[5] C. Studholme, D. L. G. Hill, and D. J. Hawkes, “Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures,” Medical Physics, vol. 24, no. 1, pp. 25–35, 1997.
[6] P. Viola and W. M. Wells III, “Alignment by maximization of mutual information,” International Journal of Computer Vision, vol. 24, no. 2, pp.137–154, 1997.
[7] P. Th´evenaz and M. Unser, “Spline pyramids for inter-modal image registration using mutual information,” in Wavelet Applications in Signal and Image Processing, A. Aldroubi, A. F. Laine, and M. A. Unser, Eds. 1997, vol. 3169 of Proc. SPIE, pp. 236–247, SPIE Press, Bellingham, WA.
[8] C. E. Rodr´ıguez-Carranza and M. H. Loew, “A weighted and deterministic entropy measure for image registration using mutual information,” in Medical Imaging: Image Processing, K. M. Hanson, Ed. 1998, vol. 3338 of Proc. SPIE, pp. 155–166, SPIE Press, Bellingham, WA.
[9] G. P. Penney, J. Weese, J. A. Little, P. Desmedt, D. L. G. Hill, and D. J. Hawkes, “A comparison of similarity measures for use in 2D-3D medical image registration,” IEEE Transactions on Medical Imaging, vol. 17, no. 4, pp. 586–595, 1999.
[10] C. Studholme, D. L. G. Hill, and D. J. Hawkes, “An overlap invariant entropy measure of 3D medical image alignment,” Pattern Recognition, vol. 32, no. 1, pp. 71–86, 1999.
[11] D. Rueckert, M. J. Clarkson, D. L. G. Hill, and D. J. Hawkes, “Non-rigid registration using higher-order mutual information,” in Medical Imaging: Image Processing, K. M. Hanson, Ed. 2000, vol. 3979 of Proc. SPIE, pp. 438–447, SPIE Press, Bellingham, WA.
[12] Khalifa F, Beache G, Gimel’farb G, Suri J, El-Baz A. State of the art medical image registration methodologies: a survey. In: Multi-modality state-of-the-art medical image segmentation and registration methodologies; 2011. p. 235–80.
[13] Svedlow M, Mc-Gillem CD, Anuta PE. Experimental examination of similarity measures and preprocessing methods used for image registration. In: Swain PH, Morrison DB, Parks DE, editors. Symposium on Machine Processing of Remotely Sensed Data. Indiana, USA; 1976. pp. 9–17.
[14] Audette MA, Ferrie FP, Peters TM. An algorithmic overview of surface registration techniques for medical imaging. Med. Image Anal. 2000; 4:201–17.
[15] Maes F, Vandermeulen D, Suetens P. Comparative evaluation of multiresolution optimization strategies for image registration by maximization of mutual information. Med. Image Anal. 1999; 3:373–86.
[16] Zhang Z. Iterative point matching for registration of free-form curves and surfaces. Int. J. Comput. Vision. 1994; 13:119–52.
[17] W. H. Press, B. P. Flannery, S. A. Teukolsky, and W. T. Vetterling, Numerical recipes in C, Cambridge University Press, Cambridge, UK, 1992.
[18] V. Espinosa-Duró, M. Faundez-Zanuy and J. Mekyska, “A New Face Database Simultaneously Acquired in Visible, Near-Infrared and Thermal Spectrums”, Cognitive Computation, vol. 5, no. 1, pp. 119-135, 2013.
[19] Pluim JPW, Maintz JBA, Viergever MA. Image registration by maximization of combined mutual information and gradient information. Medical Imaging, IEEE Transactions on. 2000; 19:809–14.