Multimodal surface matching: Fast and generalisable cortical registration using discrete optimisation

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

Volumn 7917 LNCS, Issue , 2013, Pages 475-486

  Robinson, Emma C ^a   Jbabdi, Saad ^a   Andersson, Jesper ^a   Smith, Stephen ^a   Glasser, Matthew F ^b   Van Essen, David C ^b   Burgess, Greg ^b   Harms, Michael P ^b   Barch, Deanna M ^c   Jenkinson, Mark ^a  

^a UNIVERSITY OF OXFORD   (United Kingdom)

^b WASHINGTON UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c WASHINGTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DISCRETE OPTIMISATION; GEODESIC DISTANCES; MARKOV RANDOM FIELDS; MULTI-MODAL SURFACES; MULTIPLE MODALITIES; MUTUAL INFORMATION MEASURES; RESEARCH QUESTIONS; SIMILARITY METRICS;

ALIGNMENT; DATA PROCESSING; MARKOV PROCESSES; NEUROIMAGING; OPTIMIZATION;

BRAIN;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN CORTEX; COMPUTER ASSISTED DIAGNOSIS; DIFFUSION TENSOR IMAGING; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; INFORMATION RETRIEVAL; METHODOLOGY; MYELINATED NERVE; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING; ULTRASTRUCTURE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CEREBRAL CORTEX; DIFFUSION TENSOR IMAGING; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; INFORMATION STORAGE AND RETRIEVAL; NERVE FIBERS, MYELINATED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 84887011429     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-38868-2_40     Document Type: Conference Paper

References (18)

1. Fischl, B., Sereno, M.I., Tootell, R.B.H., Dale, A.M.: High-resolution intersubject averaging and a coordinate system for the cortical surface. Human Brain Mapping 8(4), 272-284 (1999)
2. Yeo, T., Sabuncu, M., Vercauteren, T., Ayache, N., Fischl, B., Golland, P.: Spherical demons: fast diffeomorphic landmark free surface registration. IEEE Transaction on Medicial Imaging 29(3), 650-668 (2010)
3. Fischl, B., Rajendran, R., Busa, E., Augustinack, J., Hinds, O., Yeo, B.T.T., Mohlberg, H., Amunts, K., Zilles, K.: Cortical folding patterns and predicting cytoarchitecture. Cerebral Cortex 18, 1973-1983 (2008)
4. Petrović, A., Smith, S.M., Menke, R.A., Jenkinson, M.: Methods for tractography-driven surface registration of brain structures. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009, Part I. LNCS, vol. 5761, pp. 705-712. Springer, Heidelberg (2009)
5. Glocker, B., Komodakis, N., Paragios, N., Navab, N.: Dense image registration through MRFs and efficient linear programming. Medical Image Analysis 12, 731-741 (2008)
6. Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: Application to breast MR images. IEEE Transaction on Medical Imaging 18(8), 712-721 (1999)
7. Boykov, Y., Veksler, O., Sabik, R.: Fast approximate energy minimization via graph cuts. IEEE PAMI 23(11), 1-17 (2001)
8. Heinrich, M.P., Jenkinson, M., Brady, M., Schnabel, J.A.: Globally optimal deformable registration on a minimum spanning tree using dense displacement sampling. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 115-122. Springer, Heidelberg (2012)
9. Cobzas, D., Sen, A.: Random walks for deformable image registration. In: Fichtinger, G., Martel, A., Peters, T. (eds.)MICCAI 2011, Part II. LNCS, vol. 6892, pp. 557-565. Springer, Heidelberg (2011)
10. Neemuchwala, H.F.: Entropic graphs for image registration. PhD thesis, University of Michigan (2005)
11. Staring, M., van der Heide, U.A., Klein, S., Viergever, M.A., Pluim, J.P.W.: Registration of cervical mri using multifeature mutual information. IEEE Transactions on Medical Imaging 28(9), 1412-1421 (2009)
12. Glocker, B., Komodakis, N., Paragios, N., Navab, N.: Approximated curvature penalty in non-rigid registration using pairwise MRFs. In: Bebis, G., et al. (eds.) ISVC 2009, Part I. LNCS, vol. 5875, pp. 1101-1109. Springer, Heidelberg (2009)
13. Kwon, D., Lee, K.J., Yun, I.D., Lee, S.U.: Nonrigid image registration using dynamic higher-order MRF model. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part I. LNCS, vol. 5302, pp. 373-386. Springer, Heidelberg (2008)
14. Komodakis, N., Tziritas, G.: Approximate labeling via graph cuts based on linear programming. PAMI 29(8) (2007)
15. Komodakis, N., Tziritas, G.: Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal dual strategies. Computer Vision and Image Understanding 112(1), 14-29 (2008)
16. Arya, S., Mount, D., Netanyahu, N., Silverman, R., Wu, A.: An optimal algorithm for approximate nearest neighbor searching fixed dimensions. J. ACM 45(6), 891-923 (1998)
17. Glasser, M.F., Van Essen, D.C.: Mapping human cortical areas in vivo based on myelin content as revealed by T1- and T2-weighted MRI. J. Neuroscience 31(32), 11597-11616 (2011)
18. Jenkinson, M., Beckmann, C.F., Behrens, T.E.,Woolrich, M.W., Smith, S.M.: FSL. NeuroImage 62(2), 782-790 (2012)