Abstract
Registration is a key component in multi-atlas approaches to medical image segmentation. Current state of the art uses intensity-based registration methods, but such methods tend to be slow, which sets practical limitations on the size of the atlas set. In this paper, a novel feature-based registration method for affine registration is presented. The algorithm constructs an abstract representation of the entire atlas set, an überatlas, through clustering of features that are similar and detected consistently through the atlas set. This is done offline. At runtime only the feature clusters are matched to the target image, simultaneously yielding robust correspondences to all atlases in the atlas set from which the affine transformations can be estimated efficiently. The method is evaluated on 20 CT images of the heart and 30 MR images of the brain with corresponding gold standards. Our approach succeeds in producing better and more robust segmentation results compared to two baseline methods, one intensity-based and one feature-based, and significantly reduces the running times.
Chapter PDF
Similar content being viewed by others
Keywords
References
Aftab, K., Hartley, R.: Convergence of iteratively re-weighted least squares to robust M-estimators. In: IEEE Winter Conference on Applications of Computer Vision (2015)
Bay, H., Tuytelaars, T., Van Gool, L.: SURF: Speeded up robust features. Computer Vision and Image Understanding 110(3), 346–359 (2008)
Blake, A., Zisserman, A.: Visual reconstruction, p. 225. MIT Press (1987)
Chartrand, R., Yin, W.: Iteratively reweighted algorithms for compressive sensing. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3869–3872 (2008)
Chen, A., Niermann, K.J., Deeley, M.A., Dawant, B.M.: Evaluation of multiple-atlas-based strategies for segmentation of the thyroid gland in head and neck CT images for IMRT. Physics in Medicine and Biology 57(1), 93–111 (2012)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Active shape models - Their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)
Dey, D., Ramesh, A., Slomka, P.J., Nakazato, R., Cheng, V.Y., Germano, G., Bermana, D.S.: Automated algorithm for atlas-based segmentation of the heart and pericardium from non-contrast CT. In: Proceedings of SPIE (2010)
Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 24(6), 381–395 (1981)
Gill, G., Toews, M., Beichel, R.R.: Robust initialization of active shape models for lung segmentation in CT scans: A feature-based atlas approach. International Journal of Biomedical Imaging, 479154 (2014)
Hammers, A., Allom, R., Koepp, M.J., Free, S.L., Myers, R., Lemieux, L., Mitchell, T.N., Brooks, D.J., Duncan, J.S.: Three dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe. Human Brain Mapping 19(4), 224–247 (2003)
Heckemann, R.A., Keihaninejad, S., Aljabar, P., Rueckert, D., Hajnal, J.V., Hammers, A.: Improving intersubject image registration using tissue-class information benefits robustness and accuracy of multi-atlas based anatomical segmentation. NeuroImage 51(1), 221–227 (2010)
Hjärt och Lungfonden: SCAPIS - en världsunik nationell kunskapskälla (2014). http://www.hjart-lungfonden.se/scapis. (accessed September 30, 2014)
Khalifa, F., Beache, G.M., Gimel’farb, G., Suri, J.S., El-Baz, A.S.: State-of-the-art medical image registration methodologies: a survey. In: Multi modality state-of-the-art medical image segmentation and registration methodologies, pp. 235–280. Springer Science+Business Media (2011)
Kirisli, H. A., Schaap, M., Klein, S., Neefjes, L.A., Weustink, A.C., van Walsum, T., Niessen, W.J.: Fully automatic cardiac segmentation from 3D CTA data: a multi-atlas based approach. In: Proceedings of SPIE (2010)
Lee, S., Wolberg, G., Shin, S.Y.: Scattered data interpolation with multilevel B-splines. IEEE Transaction on Visualization and Computer Graphics 3(3), 228–244 (1997)
Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of International Conference on Computer Vision, vol. 2, pp. 1150–1157 (1999)
Modat, M., Ridgway, G.R., Taylor, Z.A., Lehmann, M., Barnes, J., Hawkes, D.J., Fox, N.C., Ourselin, S.: Fast free-form deformation using graphics processing units. Computer Methods and Programs in Biomedicine 98(3), 278–284 (2009)
Ourselin, S., Roche, A., Subsol, G., Pennec, X., Ayache, N.: Reconstructing a 3D structure from serial histological sections. Image and Vision Computing 19(1), 25–31 (2001)
Ourselin, S., Stefanescu, R., Pennec, X.: Robust registration of multi-modal images: towards real-time clinical applications. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002, Part II. LNCS, vol. 2489, pp. 140–147. Springer, Heidelberg (2002)
Panda S., Asman A.J., Khare S-P., Thompson L., Mawn L.A., Smith S.A., Landman B.A.: Evaluation of multi-atlas label fusion for in vivo MRI orbital segmentation. Journal of Medical Imaging 1(2) (2014)
Pham, D.L., Xu, C., Prince, J.L.: Current methods in medical image segmentation. Annual Review of Biomedical Engineering 2, 315–337 (2000)
Rueckert, D., Sonod, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Transactions on Medical Imaging 18(8), 712–721 (1999)
Svärm, L., Enqvist, O., Kahl, F., Oskarsson, M.: Improving robustness for inter-subject medical image registration using a feature-based approach. International Symposium on Biomedical Imaging (2015)
Sotiras, A., Davatzikos, C., Paragios, N.: Deformable medical image registration: A survey. IEEE Transactions on Medical Imaging 32(7) (2013)
Wang, H., Suh, J.W., Das, S.R., Pluta, J., Craige, C., Yushkevich, P.A.: Multi-atlas segmentation with joint label fusion. IEEE Transactions on Pattern Analysis and Machine Intelligence (2012)
Xu, R.: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Alvén, J., Norlén, A., Enqvist, O., Kahl, F. (2015). Überatlas: Robust Speed-Up of Feature-Based Registration and Multi-Atlas Segmentation. In: Paulsen, R., Pedersen, K. (eds) Image Analysis. SCIA 2015. Lecture Notes in Computer Science(), vol 9127. Springer, Cham. https://doi.org/10.1007/978-3-319-19665-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-19665-7_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-19664-0
Online ISBN: 978-3-319-19665-7
eBook Packages: Computer ScienceComputer Science (R0)