Abstract
Image registration aims at establishing pointwise correspondences between given images. However, in many practical applications, no correspondences can be established in certain parts of the images. A typical example is the tumor resection area in pre- and post-operative medical images. In this paper, we introduce a novel variational framework that combines registration with an automatic detection of non-correspondence regions. The formulation of the proposed approach is simple but efficient, and compatible with a large class of image registration similarity measures and regularizers. The resulting minimization problem is solved numerically with a non-alternating gradient flow scheme. Furthermore, the method is validated on synthetic data as well as axial slices of pre-, post- and intra-operative MR T1 head scans.
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
Preview
Unable to display preview. Download preview PDF.
References
Richard, F.J.: A new approach for the registration of images with inconsistent differences. In: 2004 Proceedings of the 17th International Conference on Pattern Recognition. ICPR 2004. vol. 4, pp. 649–652. IEEE (2004)
Kim, J., Fessler, J.A.: Intensity-based image registration using robust correlation coefficients. IEEE Transactions on Medical Imaging 23(11), 1430–1444 (2004)
Lu, C., Chelikani, S., Duncan, J.S.: A unified framework for joint segmentation, nonrigid registration and tumor detection: application to MR-guided radiotherapy. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 525–537. Springer, Heidelberg (2011)
Brett, M., Leff, A.P., Rorden, C., Ashburner, J.: Spatial normalization of brain images with focal lesions using cost function masking. Neuroimage 14(2), 486–500 (2001)
Hachama, M., Desolneux, A., Richard, F.J.: Bayesian technique for image classifying registration. IEEE Transactions on Image Processing 21(9), 4080–4091 (2012)
Chitphakdithai, N., Duncan, J.S.: Non-rigid registration with missing correspondences in preoperative and postresection brain images. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part I. LNCS, vol. 6361, pp. 367–374. Springer, Heidelberg (2010)
Zacharaki, E.I., Shen, D., Lee, S.-K., Davatzikos, C.: Orbit: a multiresolution framework for deformable registration of brain tumor images. IEEE Transactions on Medical Imaging 27(8), 1003–1017 (2008)
Gooya, A., Pohl, K.M., Bilello, M., Biros, G., Davatzikos, C.: Joint segmentation and deformable registration of brain scans guided by a tumor growth model. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 532–540. Springer, Heidelberg (2011)
Parisot, S., Duffau, H., Chemouny, S., Paragios, N.: Joint tumor segmentation and dense deformable registration of brain MR images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part II. LNCS, vol. 7511, pp. 651–658. Springer, Heidelberg (2012)
Kwon, D., Niethammer, M., Akbari, H., Bilello, M., Davatzikos, C., Pohl, K.: Portr: Pre-operative and post-recurrence brain tumor registration. IEEE Transactions on Medical Imaging 33(3), 651–667 (2014)
Ou, Y., Sotiras, A., Paragios, N., Davatzikos, C.: Dramms: Deformable registration via attribute matching and mutual-saliency weighting. Medical image analysis 15(4), 622–639 (2011)
Mumford, D., Shah, J.: Optimal approximation by piecewise smooth functions and associated variational problems. Communications on Pure and Applied Mathematics 42(5), 577–685 (1989)
Nikolova, M., Esedoḡlu, S., Chan, T.F.: Algorithms for finding global minimizers of image segmentation and denoising models. SIAM Journal on Applied Mathematics 66(5), 1632–1648 (2006)
Berkels, B.: An Unconstrained Multiphase Thresholding Approach for Image Segmentation. In: Tai, X.-C., Mørken, K., Lysaker, M., Lie, K.-A. (eds.) SSVM 2009. LNCS, vol. 5567, pp. 26–37. Springer, Heidelberg (2009)
Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Transactions on Image Processing 10(2), 266–277 (2001)
Fischer, B., Modersitzki, J.: Curvature based image registration. Journal of Mathematical Imaging and Vision 18(1), 81–85 (2003)
Modersitzki, J.: FAIR: flexible algorithms for image registration. SIAM, vol. 6 (2009)
Sundaramoorthi, G., Yezzi, A., Mennucci, A.: Sobolev active contours. International Journal of Computer Vision. 73(3), 345–366 (2007)
Wright, S., Nocedal, J.: Numerical optimization, vol. 2. Springer, New York (1999)
Marx, M.L., Larsen, R.J.: Introduction to mathematical statistics and its applications. Pearson/Prentice Hall (2006)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active contour models. International journal of computer vision 1(4), 321–331 (1988)
Mercier, L., Del Maestro, R.F., Petrecca, K., Araujo, D., Haegelen, C., Collins, D.L.: Online database of clinical mr and ultrasound images of brain tumors. Medical physics 39(6), 3253–3261 (2012)
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
Chen, K., Derksen, A., Heldmann, S., Hallmann, M., Berkels, B. (2015). Deformable Image Registration with Automatic Non-Correspondence Detection. In: Aujol, JF., Nikolova, M., Papadakis, N. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2015. Lecture Notes in Computer Science(), vol 9087. Springer, Cham. https://doi.org/10.1007/978-3-319-18461-6_29
Download citation
DOI: https://doi.org/10.1007/978-3-319-18461-6_29
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-18460-9
Online ISBN: 978-3-319-18461-6
eBook Packages: Computer ScienceComputer Science (R0)