Abstract
In this paper, a new similarity measure, WLMI (Weighted Local Mutual Information), based on weighted patch and mutual information is proposed to register the preoperative 3D CT model to the intra-operative 2D X-ray images in vascular interventions. We embed this metric into the 2D-3D registration framework, where we show that the robustness and accuracy of the registration can be effectively improved by adapting the strategy of local image patch selection and the weighted joint distribution calculation based on gradient. Experiments on both synthetic and real X-ray image registration show that the proposed method produces considerably better registration results in a shorter time compared with the conventional MI and Normalized MI methods.
Thanks the support by Key projects of NSFC with Grant no. 61533016.
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
Similar content being viewed by others
References
Duong, L., Liao, R., Sundar, H., Tailhades, B., Meyer, A., Xu, C.: Curve-based 2D-3D registration of coronary vessels for image guided procedure. In: International Society for Optics and Photonics, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, vol. 7261, pp. 72610S (2009)
Simonovsky, M., Gutiérrez-Becker, B., Mateus, D., Navab, N., Komodakis, N.: A deep metric for multimodal registration. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 10–18. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46726-9_2
Miao, S., Wang, Z.J., Zheng, Y., Liao, R.: Real-time 2D/3D registration via CNN regression. In: 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), pp. 1430–1434. IEEE (2016)
Roche, A., Malandain, G., Pennec, X., Ayache, N.: The correlation ratio as a new similarity measure for multimodal image registration. In: Wells, W.M., Colchester, A., Delp, S. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 1115–1124. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0056301
Shadaydeh, M., Sziranyi, T.: An improved mutual information similarity measure for registration of multi-modal remote sensing images. In: International Society for Optics and Photonics, Image and Signal Processing for Remote Sensing XXI, vol. 9643, pp. 96430F (2015)
Xuesong, L., Zhang, S., He, S., Chen, Y.: Mutual information-based multimodal image registration using a novel joint histogram estimation. Comput. Med. Imaging Graph. 32(3), 202–209 (2008)
Rubeaux, M., Nunes, J.-C., Albera, L., Garreau, M.: Edgeworth-based approximation of mutual information for medical image registration. In: 2010 2nd International Conference on Image Processing Theory Tools and Applications (IPTA), pp. 195–200. IEEE (2010)
Pradhan, S., Patra, D.: Enhanced mutual information based medical image registration. IET Image Proc. 10(5), 418–427 (2016)
Andronache, A., von Siebenthal, M., Székely, G., Cattin, P.: Non-rigid registration of multi-modal images using both mutual information and cross-correlation. Med. Image Anal. 12(1), 3–15 (2008)
Legg, P.A., Rosin, P.L., Marshall, D., Morgan, J.E.: Feature neighbourhood mutual information for multi-modal image registration: an application to eye fundus imaging. Pattern Recogn. 48(6), 1937–1946 (2015)
Russakoff, D.B., Tomasi, C., Rohlfing, T., Maurer, C.R.: Image similarity using mutual information of regions. In: Pajdla, T., Matas, J. (eds.) ECCV 2004. LNCS, vol. 3023, pp. 596–607. Springer, Heidelberg (2004). https://doi.org/10.1007/978-3-540-24672-5_47
Luan, H., Qi, F., Xue, Z., Chen, L., Shen, D.: Multimodality image registration by maximization of quantitative-qualitative measure of mutual information. Pattern Recogn. 41(1), 285–298 (2008)
Studholme, C., Hill, D.L.G., Hawkes, D.J.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recogn. 32(1), 71–86 (1999)
Kruger, J., Westermann, R.: Acceleration techniques for GPU-based volume rendering. In: Proceedings of the 14th IEEE Visualization 2003 (VIS 2003), p. 38. IEEE Computer Society (2003)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Meng, C., Wang, Q., Guan, S., Xie, Y. (2018). Weighted Local Mutual Information for 2D-3D Registration in Vascular Interventions. In: Bai, X., Hancock, E., Ho, T., Wilson, R., Biggio, B., Robles-Kelly, A. (eds) Structural, Syntactic, and Statistical Pattern Recognition. S+SSPR 2018. Lecture Notes in Computer Science(), vol 11004. Springer, Cham. https://doi.org/10.1007/978-3-319-97785-0_36
Download citation
DOI: https://doi.org/10.1007/978-3-319-97785-0_36
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-97784-3
Online ISBN: 978-3-319-97785-0
eBook Packages: Computer ScienceComputer Science (R0)