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Volumn 4584 LNCS, Issue , 2007, Pages 183-197

Segmentation of sub-cortical structures by the graph-shifts algorithm

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BRAIN; GRAPH THEORY; PARTIAL DIFFERENTIAL EQUATIONS;

EID: 34548403887     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-73273-0_16     Document Type: Conference Paper
Times cited : (18)

References (20)
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  • 8
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    • Kolmogorov, V., Zabih, R.: What Energy Functions Can Be Minimized via Graph Cuts? In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, 2352, pp. 65-81. Springer, Heidelberg (2002)
    • Kolmogorov, V., Zabih, R.: What Energy Functions Can Be Minimized via Graph Cuts? In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 65-81. Springer, Heidelberg (2002)
  • 10
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    • Multigrid and Multi-level Swendsen-Wang Cuts for Hierarchic Graph Partitions
    • Barbu, A., Zhu, S.C.: Multigrid and Multi-level Swendsen-Wang Cuts for Hierarchic Graph Partitions. IEEE Computer Vision and Pattern Recognition, 731-738 (2004)
    • (2004) IEEE Computer Vision and Pattern Recognition , vol.731-738
    • Barbu, A.1    Zhu, S.C.2
  • 12
    • 33745952825 scopus 로고    scopus 로고
    • An Integrated Framework for Image Segmentation and Perceptual Grouping
    • Tu, Z.: An Integrated Framework for Image Segmentation and Perceptual Grouping. In: International Conference on Computer Vision 2005 (2005)
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    • Tu, Z.1
  • 13
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    • Neighbor-Constrained Segmentation with Level Set Based 3D Deformable Models
    • Yang, J., Staib, L.H., Duncan, J.S.: Neighbor-Constrained Segmentation with Level Set Based 3D Deformable Models. IEEE Trans. on Medical Imaging 23(8) (2004)
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    • Yang, J.1    Staib, L.H.2    Duncan, J.S.3
  • 15
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    • A Fully Automatic and Robust Brain MRI Tissue Classification Method
    • Cocosco, C., Zijdenbos, A., Evans, A.: A Fully Automatic and Robust Brain MRI Tissue Classification Method. Medical Image Analysis 7, 513-527 (2003)
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    • Cocosco, C.1    Zijdenbos, A.2    Evans, A.3
  • 17
    • 0142192295 scopus 로고    scopus 로고
    • Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
    • Lafferty, J., McCallum, A., Pereira, F.: Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In: Int. Conference on Machine Learning 2001 (2001)
    • (2001) Int. Conference on Machine Learning
    • Lafferty, J.1    McCallum, A.2    Pereira, F.3
  • 18
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    • Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering
    • Tu, Z.: Probabilistic Boosting-Tree: Learning Discriminative Models for Classification, Recognition, and Clustering. In: International Conference on Computer Vision 2005 (2005)
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    • Tu, Z.1
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    • A Decision-Theoretic Generalization of On-line Learning and an Application to Boosting
    • Freund, Y., Schapire, R.E.: A Decision-Theoretic Generalization of On-line Learning and an Application to Boosting. Journal of Computer and System Science 55(1), 119-139 (1997)
    • (1997) Journal of Computer and System Science , vol.55 , Issue.1 , pp. 119-139
    • Freund, Y.1    Schapire, R.E.2
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    • Improved Boosting Algorithms Using Confidence-Rated Predictions
    • Schapire, R.E., Singer, Y.: Improved Boosting Algorithms Using Confidence-Rated Predictions. Machine Learning 37(3), 297-336 (1999)
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.