A discriminative method for semi-automated tumorous tissues segmentation of MR brain images

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn 2006, Issue , 2006, Pages

  Yangqiu, Song ^a   Changshui, Zhang ^a   Jianguo, Lee ^a   Fei, Wang ^a  

^a TSINGHUA UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

MR BRAIN IMAGES; PATHOLOGICAL TISSUES; TUMOROUS TISSUES SEGMENTATION; UNLABELED DATA SAMPLING;

ALGORITHMS; BRAIN; DATA REDUCTION; MAGNETIC RESONANCE; MATHEMATICAL MODELS; PROBABILITY; TISSUE;

IMAGE SEGMENTATION;

EID: 33845514134     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPRW.2006.14     Document Type: Conference Paper

References (22)

1. M. Belkin, P. Niyogi and V. Sindhwani: On Manifold Regularization. AI & Statistics, pp. 17-24, 2005
2. Y. Boykov and M. Jolly: Interactive Graph Cuts for Optimal Boundary and Region Segmentation of Objects in N-D images. ICCV, Vol. I, pp. 105-112, 2001.
3. F. Chung: Spectral Graph Theory. No. 92 in Tegional Conference Series in Mathematics. American Mathematical Society, 1997.
4. C. Fowlkes, S. Belongie, F. Chung and J. Malik: Spectral Grouping Using The Nyström Method. IEEE Trans. on PAMI, Vol. 26(2), pp. 214-225, 2004.
5. S. Geman and D. Geman: Stochastic Relaxation, Gibbs Distributions and the Bayesian Restoration of Images. IEEE Trans. on PAMI, Vol. 6(6), pp. 721-742, 1984
6. L. Grady and G. Funka-Lea: Multi-Label Image Segmentation for Medical Applications Based on Graph-Theoretic Electrical Potentials. Workshop on CVAMIA and MMBIA of ECCV, pp. 230-245, 2004.
7. N. D. Lawrence and M. I. Jordan: Semi-Supervised Learning Via Gaussian Processes. NIPS, Vol. 17, pp. 753-760, 2005.
8. Y. Li, J. Sun, C. Tang and H. Shum: Lazy Snapping. SIGGRAPH, pp. 303-308, 2004.
9. R. Malladi, J.A. Sethian and B. Vemuri: Shape Modeling With Front Propagation: A Level Set Approach. IEEE Trans. on PAMI, Vol. 17(2), pp. 158-175, 1995.
10. T. McInerney and D. Terzopoulos: Deformable Models in Medical Image Analysis: A Survey. Medical Image Analysis, Vol. 1(2), pp. 91-108, 1996.
11. D. L. Pham, C. Xu and J. L. Prince: Current methods in medical image segmentation. Annual. Review of Biomedical Engineering, Vol. 2, pp. 315-337, 2000.
12. C. Rother, V. Kolmogorov and A. Blake: "Grab Cut" - Interactive Foreground Extraction using Iterated Graph Cuts. SIGGRAPH, Vol. 23(3), pp. 309-314, 2004.
13. J. K. Udupa and S.Samarasekera: Fuzzy Connectedness And Object Definition: Theory, Algorithm And Applications In Image Segmentation. Graphical Models and Image Processing, Vol. 58(3), pp. 246-261, 1996.
14. C. Williams and D. Barber: Bayesian Classification With Gaussian Processes. IEEE Trans. on PAMI, Vol. 20(12), pp. 1342-1351, 1998.
15. C. Williams and M. Seeger: Using the Nyström Method to Speed Up Kernel Machines. NIPS, Vol. 13, pp. 682-688, 2001.
16. Q. WU, W. Dou, Y. Chen and J. Constans: Fuzzy Segementaion of Cerebral Tumorous Tissues in MR Images via Support Vector Machine and Fuzzy Clustering. IFSA.
17. C. Xu and J. L. Prince: Snakes, Shapes and Gradient Vector Flow. IEEE Trans. on Image Proc., Vol. 7(3), pp. 359-369, 1998.
18. D. Zhou, O. Bousquet, T.N. Lal, J. Weston and B. Schölkopf: Learning with Local and Global Consistency. NIPS, Vol. 16, pp. 321-328, 2003.
19. S.C. Zhu: Statistical Modeling and Conceptualization of Visual Patterns. IEEE Trans. on PAMI, Vol. 25(6), pp. 691-712, 2003.
20. X. Zhu, Z. Ghahramani and J. LaKerty: Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions. ICML, Vol. 20, pp. 912-919, 2003.
21. X. Zhu and Z. Ghahramani (2003): Semi-Supervised Learning: From Gaussian Fields to Gaussian Processes. Technical Report.
22. X. Zhu (2005): Semi-Supervised Learning Literature Survey. Technical Report.