Abstract
Because of its robustness and accuracy for a variety of applications, either monomodal or multimodal, mutual information (MI) is a very popular similarity measure for (medical) image registration. Calculation of MI is based on the joint histogram of the two images to be registered, expressing the statistical relationship between image intensities at corresponding positions. However, the calculation of the joint histogram is not straightforward. The discrete nature of digital images, sampled as well in the intensity as in the spatial domain, impedes the exact calculation of the joint histogram. Moreover, during registration often an intensity will be sought at a non grid position of the floating image.
This article compares the robustness and accuracy of two common histogram estimators in the context of nonrigid multiresolution medical image registration: a Parzen window intensity interpolator (IIP) and generalised partial volume histogram estimation (GPV). Starting from the BrainWeb data and realistic deformation fields obtained from patient images, the experiments show that GPV is more robust, while IIP is more accurate. Using a combined approach, an average registration error of 0.12 mm for intramodal and 0.30 mm for intermodal registration is achieved.
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References
Collignon, A., Maes, F., Delaere, D., Vandermeulen, D., Suetens, P., Marchal, G.: Automated multi-modality image registration based on information theory. In: Bizais, Y., Barillot, C., Di Paola, R. (eds.) Proceedings XIVth international conference on information processing in medical imaging - IPMI 1995. Computational Imaging and Vision, vol. 3, pp. 263–274. Kluwer Academic Publishers, Dordrecht (1995)
Viola, P., Wells, W.M.: Alignment by maximization of mutual information. In: ICCV 1995: Proceedings of the Fifth International Conference on Computer Vision, pp. 16–23. IEEE Computer Society, Los Alamitos (1995)
Studholme, C., Hill, D., Hawkes, D.: Automated three-dimensional registration of magnetic resonance and positron emission tomography brain images by multiresolution optimization of voxel similarity measures. Med. Phys. 24, 25–35 (1997)
West, J., Fitzpatrick, J., Wang, M., Dawant, B., Maurer Jr., C.M., Kessler, R., Maciunas, R., Barillot, C., Lemoine, D., Collignon, A., Maes, F., Suetens, P., Vandermeulen, D., van den Elsen, P., Napel, S., Sumanaweera, T., Harkness, B., Hemler, P., Hill, D., Hawkes, D., Studholme, C., Maintz, J., Viergever, M., Malandain, G., Pennec, X., Noz, M., Maguire Jr., G.M., Pollack, M., Pelizzari, C., Robb, R., Hanson, D., Woods, R.: Comparison and evaluation of retrospective intermodality brain image registration techniques. J. Comput. Assist. Tomogr. 21, 554–566 (1997)
Lehmann, T.M., Gonner, C., Spitzer, K.: Survey: Interpolation methods in medical image processing. IEEE Trans. Med. Imag. 18, 1049–1075 (1999)
Tsao, J.: Interpolation artifacts in multimodality image registration based on maximization of mutual information. IEEE Trans. Med. Imag. 22, 854–864 (2003)
Pluim, J., Maintz, J., Viergever, M.: Mutual-information-based registration of medical images: A survey. IEEE Trans. Med. Imag. 22, 986–1004 (2003)
Thevenaz, P., Unser, M.: Optimization of mutual information for multiresolution image registration. IEEE Trans. Signal Processing 9, 2083–2099 (2000)
Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Trans. Med. Imag. 16, 187–198 (1997)
Chen, H.M., Varshney, P.K.: Mutual information-based CT-MR brain image registration using generalized partial volume joint histogram estimation. IEEE Trans. Med. Imag. 22, 1111–1119 (2003)
Rueckert, D., Sonoda, L., Hayes, C., Hill, D., Leach, M., Hawkes, D.: Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Trans. Med. Imag. 18, 712–721 (1999)
Loeckx, D., Maes, F., Vandermeulen, D., Suetens, P.: Non-rigid image registration using free-form deformations with a local rigidity constraint. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 639–646. Springer, Heidelberg (2004)
Byrd, R., Lu, P., Nocedal, J., Zhu, C.: A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comput. 16, 1190–1208 (1995)
BrainWeb (1998), http://www.bic.mni.mcgill.ca/brainweb/
Collins, D., Zijdenbos, A., Kollokian, V., Sled, J., Kabani, N., Holmes, C., Evans, A.C.: Design and construction of a realistic digital brain phantom. IEEE Trans. Med. Imag. 17, 463–468 (1998)
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Loeckx, D., Maes, F., Vandermeulen, D., Suetens, P. (2006). Comparison Between Parzen Window Interpolation and Generalised Partial Volume Estimation for Nonrigid Image Registration Using Mutual Information. In: Pluim, J.P.W., Likar, B., Gerritsen, F.A. (eds) Biomedical Image Registration. WBIR 2006. Lecture Notes in Computer Science, vol 4057. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11784012_25
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DOI: https://doi.org/10.1007/11784012_25
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