A landmark-based primal-dual approach for discontinuity preserving registration

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 7601 LNCS, Issue , 2012, Pages 137-146

  Kiriyanthan, Silja ^a   Fundana, Ketut ^a   Majeed, Tahir ^a   Cattin, Philippe C ^a  

^a UNIVERSITY OF BASEL   (Switzerland)

Author keywords

liver; Motion; registration

Indexed keywords

MEDICAL FIELDS; MOTION; MOTION FIELDS; MOTION SEGMENTATION; MR IMAGES; ORGAN MOTION; PRIMAL DUAL ALGORITHMS; PRIMAL-DUAL APPROACH; QUANTITATIVE RESULT; REGISTRATION; REGISTRATION METHODS; VARIATIONAL APPROACHES;

ARTIFICIAL INTELLIGENCE; LIVER;

MAGNETIC RESONANCE IMAGING;

EID: 84868222823     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33612-6_15     Document Type: Conference Paper

References (19)

1. Amiaz, T., Kiryati, N.: Piecewise-Smooth Dense Optical Flow via Level Sets. International Journal of Computer Vision 68(2), 111-124 (2006)
2. Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High Accuracy Optical Flow Estimation Based on a Theory for Warping. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25-36. Springer, Heidelberg (2004)
3. Brox, T., Malik, J.: Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation. IEEE Transactions on Pattern Analysis and Machine Intelligence 33(3), 500-513 (2011)
4. Chambolle, A., Pock, T.: A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging. Journal of Mathematical Imaging and Vision 40(1), 120-145 (2011)
5. Chan, T.F., Esedoglu, S., Nikolova, M.: Algorithms for Finding Global Minimizers of Image Segmentation and Denoising Models. Siam Journal on Applied Mathematics 66(5), 1632-1648 (2006)
6. Demirovic, D., Serifovic, A., Cattin, P.C.: An anisotropic diffusion regularized demons for improved registration of sliding organs. In: 18th International Electrotechnical and Computer Science Conference, ERK (2009)
7. Haber, E., Heldmann, S., Modersitzki, J.: A Scale-Space Approach to Landmark Constrained Image Registration. Scale Space and Variational Methods in Computer Vision 5567, 612-623 (2009)
8. Johnson, H., Christensen, G.: Consistent Landmark and Intensity-Based Image Registration. IEEE Transactions on Medical Imaging 21(5), 450-461 (2002)
9. Kiriyanthan, S., Fundana, K., Cattin, P.C.: Discontinuity Preserving Registration of Abdominal MR Images with Apparent Sliding Organ Motion. In: Yoshida, H., Sakas, G., Linguraru, M.G. (eds.) Abdominal Imaging 2011. LNCS, vol. 7029, pp. 231-239. Springer, Heidelberg (2012)
10. Kiriyanthan, S., Fundana, K., Majeed, T., Cattin, P.C.: Discontinuity Preserving Registration through Motion Segmentation: A Primal-Dual Approach (submitted)
11. Kiriyanthan, S., Fundana, K., Majeed, T., Cattin, P.C.: A Primal-Dual Approach for Discontinuity Preserving Registration. In: 9th IEEE International Symposium on Biomedical Imaging (ISBI), pp. 350-353 (May 2012)
12. Kybic, J., Unser, M.: Fast Parametric Elastic Image Registration. IEEE Transactions on Image Processing 12(11), 1427-1442 (2003)
13. Mumford, D., Shah, J.: Optimal Approximations by Piecewise Smooth Functions and Associated Variational-Problems. Communications on Pure and Applied Mathematics 42(5), 577-685 (1989)
14. Paquin, D., Levy, D., Xing, L.: Hybrid Multiscale Landmark and Deformable Image Registration. Mathematical Biosciences and Engineering 4(4), 711-737 (2007)
15. Pock, T., Urschler, M., Zach, C., Beichel, R.R., Bischof, H.: A Duality Based Algorithm for TV-L1-Optical-Flow Image Registration. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 511-518. Springer, Heidelberg (2007)
16. Rudin, L.I., Osher, S., Fatemi, E.: Nonlinear Total Variation Based Noise Removal Algorithms. Physica D 60(1-4), 259-268 (1992)
17. Schmidt-Richberg, A., Ehrhardt, J., Werner, R., Handels, H.: Slipping Objects in Image Registration: Improved Motion Field Estimation with Direction-Dependent Regularization. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part I. LNCS, vol. 5761, pp. 755-762. Springer, Heidelberg (2009)
18. Vese, L.A., Chan, T.F.: AMultiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model. J. of Computer Vision 50(3), 271-293 (2002)
19. Yu, G., Morel, J.M.: ASIFT: An Algorithm for Fully Affine Invariant Comparison. Image Processing On Line (2011), doi: http://dx.doi.org/10.5201/ ipol.2011.my-asift