Regional manifold learning for deformable registration of brain MR images

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

Volumn 7512 LNCS, Issue , 2012, Pages 131-138

  Ye, Dong Hye ^a   Hamm, Jihun ^b   Kwon, Dongjin ^a   Davatzikos, Christos ^a   Pohl, Kilian M ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

^b OHIO STATE UNIVERSITY   (United States)

Author keywords

Brain MRI; Image registration; Manifold learning

Indexed keywords

BRAIN; DEFORMATION; IMAGE REGISTRATION; MEDICAL COMPUTING; MEDICAL IMAGING;

BRAIN MR IMAGES; BRAIN MRI; BRAIN REGIONS; DEFORMABLE REGISTRATION; MANIFOLD LEARNING; REGISTRATION ACCURACY; SEARCH SPACES; STATE-OF-THE-ART METHODS;

MAGNETIC RESONANCE IMAGING;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING;

ALGORITHMS; BRAIN; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

MLCS; MLOWN;

EID: 84872927840     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33454-2_17     Document Type: Conference Paper

References (11)

1. Ashburner, J., Friston, K.J.: Voxel-based morphometry–the methods. Neuroimage 11(6 Pt 1), 805–821 (2000)
2. Choi, Y., Lee, S.: Injectivity conditions of 2D and 3D uniform cubic B-spline functions. Graphical Models 62(6), 411–427 (2000)
3. Gerber, S., Tasdizen, T., Fletcher, P.T., Joshi, S.C., Whitaker, R.T.: Manifold modeling for brain population analysis. Med. Imag. Anal. 14(5), 643–653 (2010)
4. Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through MRFs and efficient linear programming. Med. Imag. Anal. 12(6), 731–741 (2008)
5. Hamm, J., Ye, D.H., Verma, R., Davatzikos, C.: GRAM: A framework for geodesic registration on anatomical manifolds. Med. Imag. Anal. 14(5), 633–642 (2010)
6. Heckemann, R.A., Hajnal, J.V., Aljabar, P., Rueckert, D., Hammers, A.: Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage 33(1), 115–126 (2006)
7. Jia, H., Wu, G., Wang, Q., Shen, D.: Absorb: Atlas building by self-organized registration and bundling. Neuroimage 51(3), 1057–1070 (2010)
8. Kolmogorov, V.: Convergent tree-reweighted message passing for energy minimization. Tech. Rep. MSR-TR-2004-90, Microsoft Research (MSR) (September 2004)
9. Shattuck, D.W., Mirza, M., Adisetiyo, V., Hojatkashani, C., Salamon, G., Narr, K.L., Poldrack, R.A., Bilder, R.M., Toga, A.W.: Construction of a 3D probabilistic atlas of human cortical structures. Neuroimage 39(3), 1064–1080 (2008)
10. Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Symmetric Log-Domain Diffeomorphic Registration: A Demons-Based Approach. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 754–761. Springer, Heidelberg (2008)
11. Wolz, R., Aljabar, P., Hajnal, J.V., Hammers, A., Rueckert, D.: ADNI: Leap: learning embeddings for atlas propagation. Neuroimage 49(2), 1316–1325 (2010)