Joint sulcal detection on cortical surfaces with graphical models and boosted priors

IEEE Transactions on Medical Imaging

Volumn 28, Issue 3, 2009, Pages 361-373

  Shi, Yonggang ^a   Tu, Zhuowen ^a   Reiss, Allan L ^b   Dutton, Rebecca A ^a   Lee, Agatha D ^a   Galaburda, Albert M ^c   Dinov, Ivo ^a   Thompson, Paul M ^a   Toga, Arthur W ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c HARVARD MEDICAL SCHOOL   (United States)

Author keywords

AdaBoost; Boosted prior; Cortex; Graphical model; Major sulci; Shape prior

Indexed keywords

ADAPTIVE BOOSTING; GRAPHIC METHODS; SPEECH RECOGNITION;

ADABOOST; BOOSTED PRIOR; CORTEX; GRAPHICAL MODEL; MAJOR SULCI; SHAPE PRIOR;

FEATURE EXTRACTION;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BRAIN CORTEX; BRAIN MAPPING; HISTOLOGY; HUMAN; METHODOLOGY; PROBABILITY; REPRODUCIBILITY; STATISTICAL MODEL;

ALGORITHMS; BRAIN MAPPING; CEREBRAL CORTEX; HUMANS; LOGISTIC MODELS; MARKOV CHAINS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS;

EID: 61549096564     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2008.2004402     Document Type: Article

References (48)

1. M. Ono, S. Kubik, and C. Abarnathey, Atlas of the Cerebral Sulci. New York: Thieme Medical Publishers, 1990.
2. D. C. Van Essen, "A population-average, landmark- and surface-based (PALS) atlas of human cerebral cortex," NeuroImage, vol. 28, no. 3, pp. 635-662, 2005.
3. P. M. Thompson, K. M. Hayashi, E. R. Sowell, N. Gogtay, J. N. Giedd, J. L. Rapoport, G. I. de Zubicaray, A. L. Janke, S. E. Rose, J. Semple, D. M. Doddrell, Y. Wang, T. G. M. van Erp, T. D. Cannon, and A. W. Toga, "Mapping cortical change in alzheimer's disease, brain development, and schizophrenia," NeuroImage, vol. 23, pp. S2-S18, 2004.
4. J. Perl, Probabilistic Reasoning in Intelligent Systems. San Mateo, CA: Morgan Kaufman, 1988.
5. Y. Freund and R. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," J. Comput. Syst. Sci., vol. 55, no. 1, pp. 119-139, 1997.
6. N. Khaneja, M. Miller, and U. Grenander, "Dynamic programming generation of curves on brain surfaces," IEEE Trans. Pattern Anal. Mach. Intell., vol. 20, no. 11, pp. 1260-1265, Nov. 1998.
7. A. Bartesaghi and G. Sapiro, "A system for the generation of curves on 3-D brain images," Human Brain Mapp., vol. 14, pp. 1-15, 2001.
8. L. M. Lui, Y. Wang, T. F. Chan, and P. M. Thompson, "Automatic landmark and its application, to the optimization of brain, conformal mapping," in Proc. Comput. Vision Pattern Recognit., 2006, vol. 2, pp. 1784-1792.
9. M. E. Rettmann, X. Han, C. Xu, and J. L. Prince, "Automated sulcal segmentation using watersheds on the cortical surface," NeuroImage, vol. 15, no. 2, pp. 329-244, 2002.
10. C. Kao, M. Hofer, G. Sapiro, J. Stern, K. Rehm, and D. Rotternberg, "A geometric method for automatic extraction of sulcal fundi," IEEE Trans. Med. Imag., vol. 26, no. 4, pp. 530-540, Apr. 2007.
11. H. Blum, and R. Nagel, "Shape description using weighted symmetric axis features," Pattern Recognit., vol. 10, no. 3, pp. 167-180, 1978.
12. S. M. Pizer, D. S. Fritsch, P. A. Yushkevich, V. E. Johnson, and E. L. Chaney, "Segmentation, registration, and measurement of shape variation via image object shape," IEEE Trans. Med Imag., vol. 18, no. 10, pp. 851-865, Oct. 1999.
13. T. Kong and A. Rosenfeld, "Digital topology: Introduction and survey," Comput. Vis. Graph. Image Process., vol. 48, no. 3, pp. 357-393, Dec. 1989.
14. 3," J. Math. Imag. Vis., vol. 11, pp. 207-221, 1999.
15. X. Han, C. Xu, and J. Prince, "A topology preserving level set method for geometric deformable models," IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 6, pp. 755-768, Jun. 2003.
16. J.-F. Mangin, V. Frouin, I. Bloch, J. Régis, and J. López-Krahe, "From 3-D magnetic resonance images to structural representations of the cortex topography using topology preserving deformations," J. Math. Imag. Vis., vol. 5, no. 4, pp. 297-318, 1995.
17. G. Lohmann, "Extracting line representations of sulcal and gyral patterns in MR images of the human brain," IEEE Trans. Med. Imag., vol. 17, no. 6, pp. 1040-1048, Jun. 1998.
18. M. Vaillant and C. Davatzikos, "Finding parametric representations of the cortical sulci using an active contour model," Med. Image. Anal., vol. 1, no. 4, pp. 295-315, 1996.
19. G. Goualher, E. Procyk, D. Collins, R. Venugopal, C. Barillot, and A. Evans, "Automated extraction and variability analysis of sulcal neuroanatomy," IEEE Trans. Med. Imag., vol. 18, no. 3, pp. 206-217, Mar. 1999.
20. Y. Zhou, P. M. Thompson, and A. W. Toga, "Extracting and representing the cortical sulci," IEEE Comput. Graphics Appl., vol. 19, no. 3, pp. 49-55, May/Jun. 1999.
21. X. Zeng, L. Staib, R. Schultz, H. Tagare, L. Win, and J. Duncan, "A new approach to 3-D sulcal ribbon finding from MR images," in Proc. MICCAI, 1999, pp. 148-157.
22. T. Cootes, C. Taylor, D. Cooper, and J. Graham, "Active shape models-their training and application," Computer Vis. Image. Understand., vol. 61, no. 1, pp. 38-59, 1995.
23. G. Lohmann and D. Cramon, "Automatic labelling of the human cortical surface using sulcal basins," Med. Image. Anal., vol. 4, pp. 179-188, 2000.
24. X. Tao, J. Prince, and C. Davatzikos, "Using a statistical shape model to extract sulcal curves on the outer cortex of the human brain," IEEE Trans. Med. Imag., vol. 21, no. 5, pp. 513-524, May 2002.
25. D. Rivière, J.-F. Mangin, D. Papadopoulos-Orfanos, J. Martinez, V. Frown, and J. Régis, "Automatic recognition of cortical sulci of the human brain using a congregation of neural networks," Med. Image. Anal., vol. 6, pp. 77-92, 2002.
26. Z. Tu, "Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering," in Proc. ICCV, 2005, vol. 2, pp. 1589-1596.
27. Z. Tu, S. Zheng, A. Yuille, A. Reiss, R. A. Dutton, A. Lee, A. Galaburda, I. Dinov, P. Thompson, and A. Toga, "Automated extraction of the cortical sulci based on a supervised learning approach," IEEE Trans. Med. Imag., vol. 26, pp. 541-552, Apr. 2007.
28. Surface curve protocal [Online]. Available: http://www.loni.ucla.edu/ ~esowell/edevel/new-sulcvar.html
29. S. M. Aji and R. J. McEliece, "The generalized distributive law," IEEE Trans. Inf. Theory, vol. 46, pp. 325-343, 2000.
30. S. M. Aji, G. Horn, R. J. McEliece, and M. Xu, "Iterative min-sum decoding of tail-biting codes," in Proc. IEEE Inf. Theory Workshop, 1998, pp. 68-69.
31. W. T. Freeman and E. Pasztor, "Learning to estimate scenes from images," in Proc. Neural Inf. Process. Syst. (NIPS), 1998, vol. 2, pp. 775-781.
32. A. P. Dawid, "Applications of a general propagation for probabilistic expert systems," Statistics Comput., vol. 2, pp. 25-36, 1992.
33. M. Wainwright, T. Jaakkola, and A. Willsky, "Tree consistency and bounds on the performance of the max-product algorithm and its generalizations," Statistics Comput., pp. 143-166, 2004.
34. J. C. Mazziotta, A. W. Toga, A. C. Evans, P. T. F. N. D. J. Lancaster, K. Zilles, R. P. Woods, T. Paus, G. Simpson, B. Pike, C. J. Holmes, D. L. Collins, P. M. Thompson, D. MacDonald, T. Schormann, K. Amunts, N. Palomero-Gallagher, L. Parsons, K. L. Narr, and N. Kabani, "A probabilistic atlas and reference system for the human brain: International consortium for brain mapping," Philos. Trans. R. Soc. Lond. B. Biol. Sci., vol. 356, pp. 1293-1322, 2001.
35. Y. Shi, P. Thompson, I. Dinov, and A. Toga, "Hamilton-Jacobi skeleton on cortical surfaces," IEEE Trans. Med. Imag., vol. 27, no. 5, pp. 664-673, May 2008.
36. Y. Boykov and M. P. Jolly, "Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images," in Proc. ICCV, 2001, vol. I, pp. 105-112.
37. Y. Boykov and V. Kolmogorov, "An experimental comparison of MinCut/Max-Flow algorithms for energy minimization in vision," IEEE Trans. Pattern Anal. Mach. Intell., vol. 26, no. 9, pp. .1124-1137, Sep. 2004.
38. K. Siddiqi, S. Bouix, A. Tannebaum, and S. Zuker, "Hamilton-Jacobi skeletons," Int. J. Comput. Vis., vol. 48, no. 3, pp. 215-231, 2002.
39. R. Kimmel and J. A. Sethian, "Computing geodesic paths on manifolds," Proc. Nat. Acad. Sci. USA, vol. 95, no. 15, pp. 8431-8435, 1998.
40. Y. Shi, P. M. Thompson, I. Dinov, S. Osher, and A. W. Toga, "Direct cortical mapping via solving partial differential equations on implicit surfaces,"Med. Image. Anal., vol. 11, no. 3, pp. 207-223, 2007.
41. S. Gallant, "Perceptron-based learning algorithms," IEEE Trans. Neural Networks, vol. 1, no. 2, pp. 179-191, Jun. 1990.
42. J. Friedman, T. Hastie, and R. Tibshirani, "Additive logistic regression: A statistical view of boosting," Ann. Statist., vol. 28, no. 2, pp. 337-407, 2000.
43. D. MacDonald, "A mediod for identifying geometrically simple surfaces from three dimensional images," Ph.D. dissertation, McGill Univ., Montreal, QC, Canada, 1998.
44. P. M. Thompson, A. D. Lee, R. A. Dutton, J. A. Geaga, K. M. Hayashi, M. A. Eckert, U. Bellugi, A. M. Galaburda, J. R. Korenberg, D. L. Mills, A. W. Toga, and A. L. Reiss, "Abnormal cortical complexity and thickness profiles mapped in Williams syndrome," J. Neurosci., vol. 25, no. 16, pp. 4146-4158, 2005.
45. I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," J. Mach. Learn. Res., vol. 3, pp. 1157-1182, 2003.
46. H. Peng, F. Long, and C. Ding, "Feature selection based on mutual information: Criteria of max-dependency, max-relevance, and min-redundancy, " IEEE Trans. Pattern Anal Mach. Intell., vol. 27, no. 8, pp. 1226-1238, Aug. 2005.
47. M. J. Wainwright, T. S. Jaakkola, and A. S. Willsky, "MAP estimation via agreement on trees: Message-passing and linear programming," IEEE Trans. Inf. Theory, vol. 51, no. 11, pp. 3697-3717, Nov. 2005.
48. N. Komodakis and G. Tziritas, "Approximate labeling via graph cuts based on linear programming," IEEE Trans. Pattern Anal. Mach. Intell., vol. 29, no. 8, pp. 1436-1453, Aug. 2007.