Abstract
Nonrigid registration of contrast-enhanced MR images is a difficult problem due to the change in pixel intensity caused by the wash-in and wash-out of the contrast agent. In this paper we propose a novel saliency based Markov Random Field approach for effective nonrigid registration of contrast enhanced images. Saliency information obtained from the neurobiology-based saliency model alongwith intensity information is used to quantify the degree of similarity between images in the pre- and post-contrast stages. Information from these two features is combined by using an exponential function of the saliency difference such that it assigns low values to small differences in saliency and at the same time ensures that saliency information does not bias the energy term. Rotationally-invariant edge information from edge-orientation histograms was used to complement the saliency information resulting in better registration results. Tests on real patient datasets show that our algorithm results in accurate registration. We also simulated elastic motion on images, and the deformation field recovered by our algorithm was nearly the inverse of the simulated field.
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
Bajcsy, R., Kovacic, S.: Multiresolution elastic matching. Computer Vision, Graphics, and Image Processing 46(1), 1–21 (1989)
Christensen, G., Miller, M.I., Vannier, M.: 3D brain mapping using a deformable anatomy. Phy. Med. Biol. 39, 609–618 (1994)
Bro-Nielsen, M., Gramkow, C.: Fast fluid registration of medical images. In: Höhne, K.H., Kikinis, R. (eds.) VBC 1996. LNCS, vol. 1131, pp. 267–276. Springer, Heidelberg (1996)
Thirion, J.P.: Image matching as a diffusion process: an analogy with maxwell’s demons. Med. Image Anal. 2(3), 243–260 (1998)
Meyer, C., et al.: Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations. Medical Image Analysis 1(3), 195–206 (1997)
Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast mr images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)
Rohde, G.K., Aldroubi, A., Dawant, B.M.: The adaptive bases algorithm for intensity based nonrigid image registration. IEEE Trans. Med. Imaging 22(11), 1470–1479 (2003)
Roy, S., Govindu, V.: Mrf solutions for probabilistic optical flow formulations. In: ICPR 2000: Proceedings of the International Conference on Pattern Recognition, p. 7053 (2000)
Boykov, Y., Veksler, O., Zabih, R.: Fast approximate energy minimization via graph cuts. IEEE Trans. Pattern Anal. Mach. Intell. 23(11), 1222–1239 (2001)
Shekhovtsov, A., Kovtun, I., Hlavác, V.: Efficient mrf deformation model for non-rigid image matching. In: CVPR. IEEE Computer Society, Los Alamitos (2007)
Tang, T.W.H., Chung, A.C.S.: Non-rigid image registration using graph-cuts. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 916–924. Springer, Heidelberg (2007)
Zheng, Y., Yu, J., Kambhamettu, C., Englander, S., Schnall, M.D., Shen, D.: De-enhancing the dynamic contrast-enhanced breast mri for robust registration. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol. 4791, pp. 933–941. Springer, Heidelberg (2007)
Kadir, T., Brady, M.: Saliency, scale and image description. International journal of Computer Vision 45(2), 85–105 (2001)
Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Vision Research 40, 1489–1506 (2000)
Mahapatra, D., Sun, Y.: Registration of dynamic renal mr images using neurobiological model of saliency. In: Intl. Symp. Biomed. Imaging, pp. 1119–1122 (2008)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vision 60(2), 91–110 (2004)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mahapatra, D., Sun, Y. (2008). Nonrigid Registration of Dynamic Renal MR Images Using a Saliency Based MRF Model. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2008. MICCAI 2008. Lecture Notes in Computer Science, vol 5241. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85988-8_92
Download citation
DOI: https://doi.org/10.1007/978-3-540-85988-8_92
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-85987-1
Online ISBN: 978-3-540-85988-8
eBook Packages: Computer ScienceComputer Science (R0)