Abstract
In this paper, we present a novel variational formulation of the registration assisted image segmentation problem which leads to solving a coupled set of nonlinear PDEs that are solved using efficient numerical schemes. Our work is a departure from earlier methods in that we have a unified variational principle wherein non-rigid registration and segmentation are simultaneously achieved; unlike previous methods of solution for this problem, our algorithm can accommodate for image pairs having very distinct intensity distributions. We present examples of performance of our algorithm on synthetic and real data sets along with quantitative accuracy estimates of the registration.
This research was in part funded by the NIH grants, RO1 NS046812 & NS42075. Authors thank Dr. C.M. Leonard of the UF Neuroscience Dept. for the hippocampal data sets.
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
Bansal, R., Staib, L., Chen, Z., Rangarajan, A., Knisely, J., Nath, R., Duncan, J.: Entropy-based, multiple-portal-to-3DCT registration for prostate radiotherapy using iteratively estimated segmentation. In: Taylor, C., Colchester, A. (eds.) MICCAI 1999. LNCS, vol. 1679, pp. 567–578. Springer, Heidelberg (1999)
Yezzi, A., Zollei, L., Kapur, T.: A variational framework for joint segmentation and registration. In: IEEE CVPR - MMBIA, pp. 388–400 (2001)
Wyatt, P.P., Noble, J.: Mrf-map joint segmentation and registration. In: Dohi, T., Kikinis, R. (eds.) MICCAI 2002. LNCS, vol. 2488, pp. 580–587. Springer, Heidelberg (2002)
Paragios, N., Rousson, M., Ramesh, V.: Knowledge-based registration & segmentation of the left ventricle: A level set approach. In: WACV, pp. 37–42 (2002)
Fischl, B., Salat, D., Buena, E., Albert, M., et al.: Whole brain sementation: Automated labeling of the neuroanatomical structures in the human brain. Neuron 33, 341–355 (2002)
Soatto, S., Yezzi, A.J.: Deformotion: Deforming motion, shape average and the joint registration and segmentation of images. In: ECCV, pp. 32–57 (2002)
Vemuri, B.C., Chen, Y., Wang, Z.: Registration assisted image smoothing and segmentation. In: ECCV, pp. 546–559 (2002)
Chan, T., Vesse, L.: An active contour model without edges. In: Intl. Conf. on Scale-space Theories in Computer Vision, pp. 266–277 (1999)
Wang, F., Vemuri, B.C., Rao, M., Chen, Y.: A new & robust information theoretic measure and its application to image alignment. In: IPMI, pp. 388–400 (2003)
Mcconnell brain imaging centre brain database, http://www.bic.mni.mcgill.ca/brainweb/ (1997)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2005 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, F., Vemuri, B.C. (2005). Simultaneous Registration and Segmentation of Anatomical Structures from Brain MRI. In: Duncan, J.S., Gerig, G. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2005. MICCAI 2005. Lecture Notes in Computer Science, vol 3749. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11566465_3
Download citation
DOI: https://doi.org/10.1007/11566465_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-29327-9
Online ISBN: 978-3-540-32094-4
eBook Packages: Computer ScienceComputer Science (R0)