Abstract
In many problems involving multiple image analysis, an image registration step is required. One such problem appears in brain tumor imaging, where baseline and follow-up image volumes from a tumor patient are often to-be compared. Nature of the registration for a change detection problem in brain tumor growth analysis is usually rigid or affine. Contrast enhanced T1-weighted MR images are widely used in clinical practice for monitoring brain tumors. Over this modality, contours of the active tumor cells and whole tumor borders and margins are visually enhanced. In this study, a new technique to register serial contrast enhanced T1 weighted MR images is presented. The proposed fully-automated method is based on five anatomical landmarks: eye balls, nose, confluence of sagittal sinus, and apex of superior sagittal sinus. After extraction of anatomical landmarks from fixed and moving volumes, an affine transformation is estimated by minimizing the sum of squared distances between the landmark coordinates. Final result is refined with a surface registration, which is based on head masks confined to the surface of the scalp, as well as to a plane constructed from three of the extracted features. The overall registration is not intensity based, and it depends only on the invariant structures. Validation studies using both synthetically transformed MRI data, and real MRI scans, which included several markers over the head of the patient were performed. In addition, comparison studies against manual landmarks marked by a radiologist, as well as against the results obtained from a typical mutual information based method were carried out to demonstrate the effectiveness of the proposed method.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Modersitzki, J.: Numerical methods for image registration. Oxford University Press, Oxford (2004)
Zitova, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing 21, 977–1000 (2003)
Fischer, B., Modersitzki, J.: Ill-posed medicine–an introduction to image registration. Inverse Problems 24, 1–16 (2008)
Maintz, J., Viergever, M.: A survey of medical image registration. Medical Image Analysis 2, 1–36 (1998)
Angelini, E., Clatz, O., Mandonnet, E., Konukoglu, E., Capelle, L., Duffau, H.: Glioma Dynamics and Computational Models: A Review of Segmentation, Registration, and In Silico Growth Algorithms and their Clinical Applications. Current Medical Imaging Reviews 3, 176–262 (2007)
Patriarche, J., Erickson, B.: A Review of the Automated Detection of Change in Serial Imaging Studies of the Brain. J. Digit. Imaging 17, 158–174 (2004)
Ettinger, G.J., Grimson, W.E.L., Lozano-Perez, T., Wells III, W.M., White, S.J., Kikinis, R.: Automatic registration for multiple sclerosis change detection. In: IEEE Workshop on Biomedical Image Analysis, Los Alamitos, CA, pp. 297–306 (1994)
Chui, H., Win, L., Schultz, R., Duncan, J.S., Rangarajan, A.: A unified non-rigid feature registration method for brain mapping. Med. Image. Anal. 7(2), 113–130 (2003)
Davatzikos, C., Prince, J.L.: Brain image registration based on curve mapping. In: IEEE Workshop on Biomedical Image Analysis, Los Alamitos, CA, pp. 245–254 (1994)
Davatzikos, C., Prince, J.L., Bryan, R.N.: Image registration based on boundary mapping. IEEE Transactions on Medical Imaging 15(1), 112–115 (1996)
Douglas, N.G., Bruce, F.: Accurate and robust brain image alignment using boundary-based registration. Neuroimage (2009)
Christensen, G.E.: Inverse consistent registration with object boundary constraints. In: IEEE International Symposium on Biomedical Imaging, vol. 1, pp. 591–594 (2004)
Talairach, J., Tournoux, P.: Co-planar stereotaxic Atlas of the Human Brain. Thieme Medical Publishers (1988)
Verard, L., Allain, P., Travere, J.M., Baron, J.C., Bloyet, D.: Fully Automatic Identification of AC and PC Landmarks on Brain MRI Using Scene Analysis. IEEE Transactions on Medical Imaging 16(5), 610–616 (1997)
Rohr, K., Stiehl, H., Sprengel, R., Buzug, T., Weese, J., Kuhn, M.: Landmark-based elastic registration using approximating thin-plate splines. IEEE Trans. Med. Img. 20(6), 526–534 (2001)
Thirion, J.P.: New feature points based on geometric invariants for 3d image registration. Int. J. of Computer Vision 18(2), 121–137 (1996)
Viola, P.A., Wells, W.M.: Alignment by maximization of mutual information. International Journal of Computer Vision 24(2), 137–154 (1997)
Penney, G.P., Weese, J., Little, J.A., Desmedt, P., Hill, D.L.G., Hawkes, D.J.: A comparison of similarity measures for use in 2-D-3-D medical image registration. IEEE Trans. on Med. Imaging 17, 586–594 (1998)
Pluim, J., Maintz, J., Viergever, M.: Image registration by maximization of combined mutual information and gradient information. IEEE Trans. on Med. Imaging 19(8), 809–814 (2000)
Fitzpatrick, J.M., West, J.B., Maurer, C.R.J.: Predicting error in rigid-body point-based registration. IEEE Trans. Med. Imaging 17, 694–702 (1998)
Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. Med. Image. Anal. 5(2), 143–156 (2001)
Golub, G.H., Van Loan, C.F.: Matrix Computations. JHU Press (1996)
Murray, R.M., Li, Z., Sastry, S.: A Mathematical Introduction to Robotic Manipulation. CRC Press, Boca Raton (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Demir, A., Unal, G., Karaman, K. (2010). Anatomical Landmark Based Registration of Contrast Enhanced T1-Weighted MR Images. In: Fischer, B., Dawant, B.M., Lorenz, C. (eds) Biomedical Image Registration. WBIR 2010. Lecture Notes in Computer Science, vol 6204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14366-3_9
Download citation
DOI: https://doi.org/10.1007/978-3-642-14366-3_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-14365-6
Online ISBN: 978-3-642-14366-3
eBook Packages: Computer ScienceComputer Science (R0)