Abstract
Registration of multi-modal images has been a challenging task due to the complex intensity relationship between images. The standard multi-modal approach tends to use sophisticated similarity measures, such as mutual information, to assess the accuracy of the alignment. Employing such measures imply the increase in the computational time and complexity, and makes it highly difficult for the optimization process to converge. A new registration method is proposed based on introducing a structural representation of images captured from different modalities, in order to convert the multi-modal problem into a mono-modal one. Structural features are extracted by utilizing a modified version of entropy images in a patch-based manner. Experiments are performed on simulated and real brain images from different modalities. Quantitative assessments demonstrate that better accuracy can be achieved compared to the conventional multi-modal registration method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Crum, W.R., Hartkens, T., Hill, D.L.G.: Non-rigid image registration: theory and practice. Br. J. Radiol. 2, S140–S153 (2004)
Fitzpatrick, J.M., West, J.B., Maurer Jr., C.R.: Predicting error in rigid-body point-based registration. IEEE Trans. Med. Imag. 17(5), 694–702 (1998)
Ghantous, M., Ghosh, S., Bayoumi, M.: A multi-modal automatic image registration technique based on complex wavelets. In: IEEE International Conference on Image Processing (ICIP), pp. 173–176 (2009)
Haber, E., Modersitzki, J.: Intensity gradient based registration and fusion of multi-modal images. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 726–733. Springer, Heidelberg (2006)
Kasiri, K., Clausi, D.A., Fieguth, P.: Multi-modal image registration using structural features. In: International Conference on Engineering in Medicine and Biology Society (EMBC), pp. 5550–5553 (2014)
Kasiri, K., Fieguth, P., Clausi, D.A.: Cross modality label fusion in multi-atlas segmentation. In: IEEE International Conference on Image Processing (ICIP), pp. 16–20 (2014)
Keller, Y., Averbuch, A.: Multisensor image registration via implicit similarity. IEEE Trans. Pattern Anal. Machine Intell. 28(5), 794–801 (2006)
Kim, Y.S., Lee, J.H., Ra, J.B.: Multi-sensor image registration based on intensity and edge orientation information. Pattern Recogn. 41(11), 3356–3365 (2008)
Loeckx, D., Slagmolen, P., Maes, F., Vandermeulen, D., Suetens, P.: Nonrigid image registration using conditional mutual information. IEEE Trans. Med. Imag. 29(1), 19–29 (2010)
Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: Mutual-information-based registration of medical images: a survey. IEEE Trans. Med. Imag. 22(8), 986–1004 (2003)
Rivaz, H., Karimaghaloo, Z., Collins, D.L.: Self-similarity weighted mutual information: a new nonrigid image registration metric. Med. Image Anal. 18(2), 343–358 (2014)
Rivaz, H., Karimaghaloo, Z., Fonov, V.S., Collins, D.L.: Nonrigid registration of ultrasound and mri using contextual conditioned mutual information. IEEE Trans. Med. Imag. 33(3), 708–725 (2014)
BrainWeb: simulated brain database. http://www.bic.mni.mcgill.ca/brainweb/
ITK: Image Registration and Segmentation Toolkit. www.itk.org
RIRE: Retrospective Image Registration Evaluation. http://www.insight-journal.org/rire/
Wachinger, C., Navab, N.: Structural image representation for image registration. In: Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 23–30 (2010)
Wells, W.M., Viola, P., Atsumi, H., Nakajima, S., Kikinis, R.: Multi-modal volume registration by maximization of mutual information. Med. Image Anal. 1(1), 35–51 (1996)
Wong, A., Clausi, D.A., Fieguth, P.: CPOL: complex phase order likelihood as a similarity measure for MR- CT registration. Med. Image Anal. 14(1), 50–57 (2010)
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
Kasiri, K., Fieguth, P., Clausi, D.A. (2015). Structural Representations for Multi-modal Image Registration Based on Modified Entropy. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2015. Lecture Notes in Computer Science(), vol 9164. Springer, Cham. https://doi.org/10.1007/978-3-319-20801-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-20801-5_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-20800-8
Online ISBN: 978-3-319-20801-5
eBook Packages: Computer ScienceComputer Science (R0)