Fast Brain MRI Registration with Automatic Landmark Detection Using a Single Template Image
Automatic registration of brain MR images is still a challenging problem. We have chosen an approach based on landmarks matching. However, manual landmarking of the images is cumbersome. Existing algorithms for automatic identification of pre-defined set of landmarks usually require manually landmarked training bases. We propose the registration algorithm that involves automatic detection of landmarks with the use of only one manually landmarked template image. Landmarks are detected using Canny edge detector and point descriptors. Evaluation of four types of descriptors showed that SURF provides the best trade-off between speed and accuracy. Thin plate spline transformation is used for landmark-based registration. The proposed algorithm was compared with the best existing registration algorithm without the use of local features. Our algorithm showed significant speed-up and better accuracy in matching of anatomical structures surrounded by the landmarks. All the experiments were performed on the IBSR database.
The authors would like to thank Alexey V. Petraikin, M.D. from Pirogov Russian National Research Medical University (RNRMU) for valuable discussion.
- 1.Insight segmentation and registration toolkit (ITK). http://www.itk.org
- 2.Internet brain segmentation repository. http://www.nitrc.org/projects/ibsr
- 3.Andersson, J., Smith, S., Jenkinson, M.: FNIRT – FMRIB’s non-linear image registration tool. Human Brain Mapping. Poster 496 (2008)Google Scholar
- 10.Diez, Y., Gubern-Mérida, A., Wang, L., Diekmann, S., Martí, J., Platel, B., Kramme, J., Martí, R.: Comparison of methods for current-to-prior registration of breast DCE-MRI. In: Fujita, H., Hara, T., Muramatsu, C. (eds.) IWDM 2014. LNCS, vol. 8539, pp. 689–695. Springer, Heidelberg (2014) Google Scholar
- 11.Evans, A.C., Dai, W., Collins, L., et al.: Warping of a computerized 3-D atlas to match brain image volumes for quantitative neuroanatomical and functional analysis. In: Proceedings SPIE, pp. 236–246. SPIE Press, Bellingham (1991)Google Scholar
- 12.Guerrero, R., Pizarro, L., Wolz, R., Rueckert, D.: Landmark localization in brain mr images using feature point descriptors based on 3D local self-similarities. In: 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1535–1538. IEEE Press, New York (2012)Google Scholar
- 13.Han, D., Gao, Y., Wu, G., Yap, P.-T., Shen, D.: Robust anatomical landmark detection for MR brain image registration. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds.) MICCAI 2014, Part I. LNCS, vol. 8673, pp. 186–193. Springer, Heidelberg (2014) Google Scholar
- 19.Shechtman, E., Irani, M.: Matching local self-similarities across images and videos. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE Press, New York (2007)Google Scholar
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