Abstract
In this work, we propose a novel fully automated method to solve the 3D multimodal non-rigid image registration problem. The proposed strategy overcomes the monomodal intensity restriction of fluid-like registration (FLR) models, such as Demons-based registration algorithms, by applying a mapping that relies on an intensity uncertainty quantification in a local neighbourhood, bringing the target and source images into a common domain where they are comparable, no matter their image modalities or mismatched intensities between them. The proposed methodology was tested with T1, T2 and PD weighted brain magnetic resonance (MR) images with synthetic deformations, and CT-MR brain images from a radiotherapy clinical case. The performance of the proposed approach was evaluated quantitatively by standard indices that assess the correct alignment of anatomical structures of interest. The results obtained in this work show that the addition of the local uncertainty mapping properly resolve the monomodal restriction of FLR algorithms when same anatomic counterparts exists in the images to register, and suggest that the proposed strategy can be an option to achieve multimodal 3D registrations.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zitová, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21, 977–1000 (2003)
Modersitzki, J.: Numerical Methods for Image Registration. Oxford University Press (2004)
Crum, W., Hartkens, T., Hill, D.: Non-rigid image registration: theory and practice. British Journal of Radiology 77(spec. iss. 2), S140 – S153 (2004)
Mani, V., Rivazhagan, D.: Survey of medical image registration. Journal of Biomedical Engineering and Technology 1(2), 8–25 (2013)
Rueckert, D., Aljabar, P.: Nonrigid registration of medical images: Theory, methods, and applications. IEEE Signal Processing Magazine 27(4), 113–119 (2010)
Vásquez-Osorio, E.M., Hoogeman, M.S., Bondar, L., Levendag, P.C., Heijmen, B.J.M.: A novel flexible framework with automatic feature correspondence optimization for nonrigid registration in radiotherapy. Medical Physics 36(7), 2848–2859 (2009)
Pluim, J.P.W., Maintz, J., Viergever, M.: Mutual-information-based registration of medical images: a survey. IEEE Transactions on Medical Imaging 22(8), 986–1004 (2003)
Zhuang, X., Arridge, S., Hawkes, D., Ourselin, S.: A nonrigid registration framework using spatially encoded mutual information and free-form deformations. IEEE Transactions on Medical Imaging 30(10), 1819–1828 (2011)
Hui, W., Yong, Y., Hongjun, W., Guanzhong, G.: A modified optical flow based method for registration of 4d ct data of hepatocellular carcinoma patients. In: 2012 IEEE International Conference on Virtual Environments Human-Computer Interfaces and Measurement Systems (VECIMS), pp. 21–25 (2012)
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: Efficient non-parametric image registration. NeuroImage 45(1, suppl. 1), S61 – S72 (2009)
Janssens, G., Jacques, L., de Xivry, J.O., Geets, X., Macq, B.: Diffeomorphic registration of images with variable contrast enhancement. International Journal of Biomedical Imaging 2011, 3:1–3:12 (2011)
Baker, S., Scharstein, D., Lewis, J., Roth, S., Black, M., Szeliski, R.: A database and evaluation methodology for optical flow. International Journal of Computer Vision 92(1), 1–31 (2011)
Arce-Santana, E., Campos-Delgado, D.U., Alba, A.: A non-rigid multimodal image registration method based on particle filter and optical flow. In: Bebis, G., et al. (eds.) ISVC 2010, Part I. LNCS, vol. 6453, pp. 35–44. Springer, Heidelberg (2010)
Thirion, J.P.: Image matching as a diffusion process: an analogy with maxwell’s. Medical Image Analysis 2, 243–260 (1988)
Reducindo, I., Mejia-Rodriguez, A.R., Arce-Santana, E.R., Campos-Delgado, D.U., Vigueras-Gomez, F., Scalco, E., Bianchi, A.M., Cattaneo, G.M., Rizzo, G.: Multimodal non-rigid registration methods based on local variability measures in computed tomography and magnetic resonance brain images. IET Image Processing (2014)
Nyul, L., Udupa, J., Zhang, X.: New variants of a method of mri scale standardization. IEEE Transactions on Medical Imaging 19(2), 143–150 (2000)
Kwan, R., Evans, A., Pike, G.: Mri simulation-based evaluation of image-processing and classification methods. IEEE Transactions on Medical Imaging 18(11), 1085–1097 (1999)
Heimann, T., Van Ginneken, B., Styner, M., Arzhaeva, Y., Aurich, V., Bauer, C., et al.: Comparison and evaluation of methods for liver segmentation from ct datasets. IEEE Transactions on Medical Imaging 28(8), 1251–1265 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Reducindo, I. et al. (2014). Multimodal Non-Rigid Registration Methods Based on Demons Models and Local Uncertainty Quantification Used in 3D Brain Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2014. Lecture Notes in Computer Science, vol 8888. Springer, Cham. https://doi.org/10.1007/978-3-319-14364-4_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-14364-4_2
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14363-7
Online ISBN: 978-3-319-14364-4
eBook Packages: Computer ScienceComputer Science (R0)