Abstract
A new similarity measure for volume registration is proposed, which uses using the assumption that the joint distribution of a target tissue is known. This similarity measure is designed so that it can deal with the tissue slide that occurs at boundaries between the target tissue and other tissues. Pre-segmentation of the target tissue is unnecessary. We intend to apply the proposed measure to registering volumes acquired at different time-phases in dynamic CT scans of the liver using contrast materials. In order to derive the similarity measure, we first formulate the ideal case where the joint distributions of all the tissues are known, after which we derive the measure for a realistic case where only the joint distribution of the target tissue is known. We applied the proposed measure experimentally to eight dynamic CT data sets of the liver. After describing a practical method for estimating the joint distribution of the liver from real CT data, we show that the problem of tissue slide is effectively dealt with using the proposed measure.
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
Andress Carrillo, Jefrey L. Duerk, Jonathan S. Lewin, and David L. Wilson. Semiautomatic 3-D Image Registration as Applied to Interventional MRI Liver Cancer Treatment. IEEE Trans. Med. Imaging, 19(3):175–185, 2000.
D. Rueckert, L.I. Sonoda, C. Hayes, D.L.G. Hill, M.O. Leach, D.J. Hawkes. Nonrigid Registration Using Free-Form Deformations: Application to Breast MR Images. IEEE Trans. Med. Imaging, 18(8):712–721, 1999.
H. Lester and S.R. Aridge. A survey of hierarchical non-linear medical image registration. Pattern Recognition, 32:71–86, 1999.
Mi Chen, Takeo Kanade, Dean Pomerleau, Jeff Schneider. 3D Deformable Registration of Medical Images Using a Statistical Atlas. Lecture Notes in Computer Science, 1679 (MICCAI’99): 621–630, 1999.
Yongmei Wang, Lawrence H Staib. Physical model-based non-rigid registration incorporation statistical shape information. Medical Image Analysis, 4:7–20, 2000.
Andrea Schenk, Guido Prause, and Heinz-Otto Peitgen. Efficient Semiautomatic Segmentation of 3D Objects in Medical Images. Lecture Notes in Computer Science, 1935 (MICCAI2000): 186–195, 2000.
William M. WellsIII, Paul Viola, Hideki Atsumi, Shin Nakajima and Ron Kikinis. Multi Modal volume registration by maximization of mutual information. Medical Image Analysis, 1(1):35–51, 1996.
Josien P.W. Pluim, J.B. Antoine Maintz, and Max A. Viergever. Interpolation Artifacts in Mutual Information-Based Image Registration. Computer Vision and Image Understanding, 77:211–232, 2000.
Mark Holden, Derek L. G. Hill, Erika R. E. Denton, Jo M. Jarosz, Tim C. S. Cox, Trosten Rohlfing, Joanne, Goodey, David J. Hawkes. Voxel Similarity Measures for 3-D Serial MR Brain Image Registration. IEEE Trans. Med. Imaging, 19(2):94–102, 2000.
Alexis Roche, Greegoire Malandain, Nicholas Ayache, and Sylvain Prima. Toward a Better Comprehension of Similarity Measures Used in Medical Image Registration. Lecture Notes in Computer Science, 1679 (MICCAI’99): 555–566, 1999.
Michael E. Leventon and W.Eric L. Grimson. Multi-Modal Volume Registration Using Joint Intensity distributions. Lecture Notes in Computer Science, 1496 (MIC-CAI’98): 1057–1066, 1998.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Masumoto, J. et al. (2002). A New Similarity Measure for Nonrigid Volume Registration Using Known Joint Distribution of Target Tissue: Application to Dynamic CT Data of the Liver. In: Dohi, T., Kikinis, R. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002. MICCAI 2002. Lecture Notes in Computer Science, vol 2489. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45787-9_62
Download citation
DOI: https://doi.org/10.1007/3-540-45787-9_62
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-44225-7
Online ISBN: 978-3-540-45787-9
eBook Packages: Springer Book Archive