Neighbourhood approximation forests

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

Volumn 7512 LNCS, Issue , 2012, Pages 75-82

  Konukoglu, Ender ^a   Glocker, Ben ^a   Zikic, Darko ^a   Criminisi, Antonio ^a  

^a MICROSOFT RESEARCH   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; FORESTRY; LARGE DATASET; MAGNETIC RESONANCE IMAGING; MEDICAL COMPUTING;

AGE PREDICTIONS; APPLICATION SPECIFIC; APPROXIMATION QUALITY; COMPUTATIONAL ADVANTAGES; EXTRACT INFORMATIONS; HIGH-DIMENSIONAL IMAGES; IMAGE INTENSITIES; NEIGHBOURHOOD SEARCH;

MEDICAL IMAGING;

AGING; ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; HISTOLOGY; IMAGE ENHANCEMENT; INFORMATION RETRIEVAL; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; STATISTICAL ANALYSIS;

AGING; ALGORITHMS; ARTIFICIAL INTELLIGENCE; BRAIN; DATA INTERPRETATION, STATISTICAL; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; MAGNETIC RESONANCE IMAGING; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

MLCS; MLOWN;

EID: 84872944419     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-33454-2_10     Document Type: Conference Paper

References (20)

1. Aljabar, P., Heckemann, R., Hammers, A., Hajnal, J., Rueckert, D.: Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. Neuroimage 46(3), 726–738 (2009)
2. Aljabar, P., Wolz, R., Rueckert, D.: Manifold Learning for Medical Image Registration, Segmentation, and Classification. In: Machine Learning in Computer-Aided Diagnosis: Medical Imaging Intelligence and Analysis. IGI Global (2012)
3. Allassonnire, S., Amit, Y., Trouv, A.: Towards a coherent statistical framework for dense deformable template estimation. J. R. Stat. Soc.: Series B 69 (2007)
4. Amit, Y., Geman, D.: Shape quantization and recognition with randomized trees. Neural Computation 9 (1997)
5. Bengio, Y., Paiement, J., Vincent, P., Delalleau, O., Le Roux, N., Ouimet, M.: Out-of-sample extensions for lle, isomap, mds, eigenmaps, and spectral clustering. In: NIPS, vol. 16 (2004)
6. Breiman, L.: Random forests. Machine Learning 45 (2001)
7. Coupe, P., Manjon, J.V., Fonov, V., Pruessner, J., Robles, M., Collins, D.L.: Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. Neuroimage 54 (2011)
8. Criminisi, A., Shotton, J., Konukoglu, E.: Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. NOW Publishing: Foundations and Trends 7 (2012)
9. 9.Gray, K.R., Aljabar, P., Heckemann, R.A., Hammers, A., Rueckert, D.:Random Forest-Based Manifold Learning for Classification of Imaging Data in Dementia. In: Suzuki, K., Wang, F., Shen, D., Yan, P. (eds.) MLMI 2011. LNCS, vol. 7009, pp. 159–166. Springer, Heidelberg (2011)
10. Hamm, J., Ye, D.H., Verma, R., Davatzikos, C.: GRAM: A framework for geodesic registration on anatomical manifolds. Med. Image Anal. 14 (2010)
11. Jia, H., Yap, P.T., Shen, D.: Iterative multi-atlas-based multi-image segmentation with tree-based registration. Neuroimage 59 (2012)
12. Marcus, D., Wang, T., Parker, J., Csernansky, J., Morris, J., Buckner, R.: Open access series of imaging studies (oasis): cross-sectional mri data in young, middle aged, nondemented, and demented older adults. J. of Cog. Neuroscience 19 (2007)
13. Nister, D., Stewenius, H.: Scalable recognition with a vocabulary tree. In: CVPR, vol. 2, pp. 2161–2168 (2006)
14. Niyogi, X.: Locality preserving projections. In: NIPS, vol. 16 (2004)
15. Norouzi, M., Fleet, D.: Minimal loss hashing for compact binary codes. In: ICML (2011)
16. Sabuncu, M.R., Balci, S.K., Shenton, M.E., Golland, P.: Image-driven population analysis through mixture modeling. IEEE Trans. Med. Imaging 28 (2009)
17. Sabuncu, M.R., Van Leemput, K.: The Relevance Voxel Machine (RVoxM): A Bayesian Method for Image-Based Prediction. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 99–106. Springer, Heidelberg (2011)
18. Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: Efficient non-parametric image registration. NeuroImage 45 (2009)
19. Weiss, Y., Torralba, A., Fergus, R.: Spectral hashing. In: NIPS (2008)
20. Wolz, R., Aljabar, P., Hajnal, J.V., Hammers, A., Rueckert, D., Weiner, M.W.: LEAP: learning embeddings for atlas propagation. Neuroimage 49 (2010)