Clustering by optimum path forest and its application to automatic GM/WM classification in MR-T1 images of the brain

2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Proceedings, ISBI

Volumn , Issue , 2008, Pages 428-431

  Cappabianco, Fábio A M ^a   Falcão, Alexandre X ^a   Rocha, Leonardo M ^a  

^a UNIVERSITY OF CAMPINAS   (Brazil)

Author keywords

Graph cut measures; Image foresting transform; Improved mean shift algorithm; Medical image processing; MR image segmentation

Indexed keywords

CHLORINE COMPOUNDS;

GRAPH-CUT MEASURES; IMAGE FORESTING TRANSFORM; IMPROVED MEAN-SHIFT ALGORITHM; MEDICAL IMAGE PROCESSING; MR IMAGE SEGMENTATION;

IMAGE ENHANCEMENT;

EID: 51049106600     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2008.4541024     Document Type: Conference Paper

References (17)

1. L.M. Rocha, A.X. Falcão, and L.G.P. Meloni, "A robust extension of the mean shift algorithm using optimum path forest," in Proc. of the 12th Intl. Workshop on Combinatorial Image Analysis, 2008, to appear.
2. Y. Cheng, "Mean shift, mode seeking, and clustering," IEEE TPAMI, vol. 17, no. 8, pp. 790-799, Aug 1995.
3. A. X. Falcão, J. Stolfi, and R. A. Lotufo, "The image foresting transform: theory, algorithms, and applications," IEEE TPAMI, vol. 26, no. 1, pp. 19-29, Jan 2004.
4. J. Shi and J. Malik, "Normalized cuts and image segmentation," IEEE TPAMI, vol. 22, no. 8, pp. 888-905, Aug 2000.
5. Y. Y. Boykov and M. P. Jolly, "Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images," in Proc. of the ICCV, 2001, vol. 1, pp. 105-112.
6. A. X. Falcão and F. P. G. Bergo, "Interactive volume segmentation with differential image foresting transforms," IEEE TMI, vol. 23, no. 9, pp. 1100-1108, Sep 2004.
7. F.P.G. Bergo, A.X. Falcão, P.A.V. Miranda, and L.M. Rocha, "Automatic image segmentation by tree pruning," Journal of Mathematical Imaging and Vision, 2007, to appear.
8. F.L. Bookstein, "Voxel-based morphometry should not be used with imperfectly registered images," Neuroimage, vol. 14, no. 6, pp. 1454-1462, 2001.
9. N. Thacker, "A critical analysis of voxel-based morphometry," Tech. Rep., Tina Memo, 2005.
10. Y. Zhang, M. Brady, and S. Smith, "Segmentation of brain MR images through a hidden markov randomfield model and the expectation-maximization algorithm," IEEE TMI, vol. 20, no. 1, pp. 45-57, 2001.
11. V. Grau, A.U.J. Mewes, M. Alcaniz, R. Kikinis, and S.K. Warfield, "Improved watershed transform for medical image segmentation using prior information," IEEE TMI, vol. 23, no. 4, pp. 447-458, 2004.
12. K. Van Leemput, F. Maes, D. Vandermeulen, and P. Suetens, "A unifying framework for partial volume segmentation of brain mr images," Medical Imaging, IEEE Transactions on, vol. 22, no. 1, pp. 105-119, 2003.
13. S. Bricq, C. Collet, and J.P. Armspach, "Triplet markov chain for 3DMRI brain segmentation using a probabilistic atlas," 3rd IEEE International Symposium on Biomedical Imaging: From Macro to Nano, 2006, pp. 386-389, 2006.
14. SP. Awate, T. Tasdizen, N. Foster, and RT. Whitaker, "Adaptive markov modeling for mutual-information-based, unsupervised MRI brain-tissue classification," Medical Image Analysis, vol. 10, no. 5, pp. 726-39, 2006.
15. M. Prastawa, J.H. Gilmore, W. Lin, and G. Gerig, "Automatic segmentation of MR images of the developing newborn brain," Medical Image Analysis, vol. 9, no. 5, pp. 457-66, 2005.
16. K.M. Pohl, S. Bouix, M. Nakamura, T. Rohlfing, R.W. McCarley, R. Kikinis, W.E.L. Grimson, M.E. Shenton, and W.M. Wells, "A hierarchical algorithm for MR brain image parcellation," IEEE TMI, vol. 26, no. 9, pp. 1201-1212, 2007.
17. D. Comaniciu, "An algorithm for data-driven bandwidth selection," IEEE TPAMI, vol. 25, no. 2, pp. 281-288, 2003.