Unsupervised deep feature learning for deformable registration of MR brain images

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

Volumn 8150 LNCS, Issue PART 2, 2013, Pages 649-656

  Wu, Guorong ^a   Kim, Minjeong ^a   Wang, Qian ^a   Gao, Yaozong ^a   Liao, Shu ^a   Shen, Dinggang ^a  

^a UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANATOMICAL VARIATIONS; CONVOLUTIONAL NETWORKS; CORRESPONDENCE DETECTION; DEEP FEATURE LEARNING; DEFORMABLE REGISTRATION; HIERARCHICAL REPRESENTATION; LEARNING-BASED METHODS; MORPHOLOGICAL SIGNATURES;

ARTIFICIAL INTELLIGENCE; COMPUTER SCIENCE;

IMAGE REGISTRATION;

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

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

EID: 84892841517     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40763-5_80     Document Type: Conference Paper

References (12)

1. Zitová, B., Flusser, J.: Image registration methods: A survey. Image and Vision Computing 21(11), 977-1000 (2003)
2. Vercauteren, T., et al.: Diffeomorphic demons: efficient non-parametric image registration. NeuroImage 45(1, suppl. 1), S61-S72 (2009)
3. Liu, J., Vermuri, B.C., Marroquin, J.L.: Local frequency representations for robust multimodal image registration. IEEE Trans. Med. Imaging 21(5), 462-469 (2002)
4. Ou, Y., et al.: DRAMMS: Deformable registration via attribute matching and mutual-saliency weighting. Med. Image Anal. 15(4), 622-639 (2011)
5. Wu, G., Qi, F., Shen, D.: Learning-based deformable registration of MR brain images. IEEE Trans. Med. Imaging, 25(9), 1145-1157 (2006)
6. Wu, G., Qi, F., Shen, D.: Learning best features and deformation statistics for hierarchical registration of MR brain images. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 160-171. Springer, Heidelberg (2007)
7. Le, Q.V., et al.: Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2011)
8. Hyvarinen, A., Hoyer, P.: Emergence of phase- and shift-invariant features by decomposition of natural images into independent feature subspaces. Neural Comput. 12(7), 1705-1720 (2000)
9. LeCun, Y., Bengio, Y.: Convolutional network for images, speech, and time series. In: The Handbook of Brain Theory and Neural Networks (1995)
10. Peyrat, J., et al.: Registration of 4D cardiac CT sequences under trajectory constraints with multichannel diffeomorphic demons. IEEE Trans. Med. Imaging 29(7), 1351-1368 (2010)
11. Shen, D.G.: Image registration by local histogram matching. Pattern Recognition 40(4), 1161-1172 (2007)
12. Peyrat, J.-M., Delingette, H., Sermesant, M., Pennec, X., Xu, C., Ayache, N.: Registration of 4D time-series of cardiac images with multichannel Diffeomorphic Demons. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 972-979. Springer, Heidelberg (2008)