Abstract
Current longitudinal image registration methods rely on the assumption that image appearance between time-points remains constant or changes uniformly within intensity classes. This assumption, however, is not valid for magnetic resonance imaging of brain development. Registration methods developed to align images with non-uniform appearance change either (i) locally minimize some global similarity measure, or (ii) iteratively estimate an intensity transformation that makes the images similar. However, these methods treat the individual images as independent static samples and are inadequate for the strong non-uniform appearance changes seen in neurodevelopmental data. Here, we propose a model-based similarity measure intended for aligning longitudinal images that locally estimates a temporal model of intensity change. Unlike previous approaches, the model-based formulation is able to capture complex appearance changes between time-points and we demonstrate that it is critical when using a deformable transformation model.
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
Barkovich, A.J., Kjos, B.O., Jackson, D.E., Norman, D.: Normal maturation of the neonatal and infant brain: MR imaging at 1.5T. Radiology 166, 173–180 (1988)
Durrleman, S., Pennec, X., Trouvé, A., Gerig, G., Ayache, N.: Spatiotemporal Atlas Estimation for Developmental Delay Detection in Longitudinal Datasets. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part I. LNCS, vol. 5761, pp. 297–304. Springer, Heidelberg (2009)
Friston, K., Ashburner, J., Frith, C., Poline, J., Heather, J.D., Frackowiak, R.: Spatial registration and normalization of images. Human Brain Mapping 2, 165–189 (1995)
Kinney, H.C., Karthigasan, J., Borenshteyn, N.I., Flax, J.D., Kirschner, D.A.: Myelination in the developing human brain: biochemical correlates. Neurochem Res. 19(8), 983–996 (1994)
Loeckx, D., Slagmolen, P., Maes, F., Vandermeulen, D., Suetens, P.: Nonrigid image registration using conditional mutual information. IEEE Transactions on Medical Imaging 29(1), 19–29 (2010)
Niethammer, M., Huang, Y., Vialard, F.-X.: Geodesic Regression for Image Time-Series. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 655–662. Springer, Heidelberg (2011)
Roche, A., Guimond, A., Ayache, N., Meunier, J.: Multimodal Elastic Matching of Brain Images. In: Vernon, D. (ed.) ECCV 2000, Part II. LNCS, vol. 1843, pp. 511–527. Springer, Heidelberg (2000)
Sampaio, R.C., Truwit, C.L.: Myelination in the developing brain. In: Handbook of Developmental Cognitive Neuroscience, pp. 35–44. MIT Press (2001)
Studholme, C., Drapaca, C., Iordanova, B., Cardenas, V.: Deformation-based mapping of volume change from serial brain MRI in the presence of local tissue contrast change. IEEE Transactions on Medical Imaging 25(5), 626–639 (2006)
Viola, P., Wells III, W.M.: Alignment by maximization of mutual information. In: Proc. Conf. Fifth Int. Computer Vision, pp. 16–23 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Csapo, I., Davis, B., Shi, Y., Sanchez, M., Styner, M., Niethammer, M. (2012). Temporally-Dependent Image Similarity Measure for Longitudinal Analysis. In: Dawant, B.M., Christensen, G.E., Fitzpatrick, J.M., Rueckert, D. (eds) Biomedical Image Registration. WBIR 2012. Lecture Notes in Computer Science, vol 7359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31340-0_11
Download citation
DOI: https://doi.org/10.1007/978-3-642-31340-0_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31339-4
Online ISBN: 978-3-642-31340-0
eBook Packages: Computer ScienceComputer Science (R0)