A fuzzy, nonparametric segmentation framework for DTI and MRI analysis: With applications to DTI-tract extraction

IEEE Transactions on Medical Imaging

Volumn 26, Issue 11, 2007, Pages 1525-1536

  Awate, Suyash P ^a   Zhang, Hui ^a   Gee, James C ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

Diffusion tensor imaging (DTI); Fuzzy sets; Image segmentation; Information theory; Magnetic resonance imaging (MRI); Markov random fields; Nonparametric modeling; Riemannian statistics

Indexed keywords

DIFFUSION; IMAGE SEGMENTATION; INFORMATION THEORY; MAGNETIC RESONANCE IMAGING; MARKOV PROCESSES; OPTIMIZATION; STATISTICS; TENSORS;

DIFFUSION TENSOR IMAGING (DTI); FUZZY SEGMENTATION; MARKOV RANDOM FIELDS; NONPARAMETRIC MODELING; RIEMANNIAN STATISTICS;

FUZZY SETS;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BRAIN; COMPUTER ASSISTED DIAGNOSIS; DIFFUSION WEIGHTED IMAGING; FUZZY LOGIC; HISTOLOGY; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; MYELINATED NERVE; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THREE DIMENSIONAL IMAGING; ULTRASTRUCTURE;

ALGORITHMS; BRAIN; DIFFUSION MAGNETIC RESONANCE IMAGING; FUZZY LOGIC; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; IMAGING, THREE-DIMENSIONAL; NERVE FIBERS, MYELINATED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 35648993039     PISSN: 02780062     EISSN: None     Source Type: Journal    
DOI: 10.1109/TMI.2007.907301     Document Type: Article

References (54)

1. S. Mori and P. van Zijl, "Fiber tracking: Principles and stratgies - A technical review," NMR Biomed, vol. 15, pp. 468-480, 2002.
2. C. Lenglet, M. Rousson, R. Deriche, O. Faugeras, S. Lehericy, and K. Ugurbil, "A Riemannian approach to diffusion tensor image segmentation," in Proc. Info. Proc. Med. Imag., 2005, pp. 591-602.
3. Z. Wang and B. Vemuri, "DTI segmentation using an information theoretic tensor dissimilarity measure," IEEE Trans. Med. Imag., vol. 24, no. 10, pp. 1267-1277, Oct. 2005.
4. L. Concha, D. Gross, and C. Beulieu, "Diffusion tensor tractography of the limbic system," Amer. J. Neuroradiol., vol. 26, pp. 2267-2274, 2005.
5. Gong, Jiang, Zhu, Zang, Wang, Xie, Xiao, and Guo, "Asymmetry analysis of cingulum based on scale-invariant parameterization by diffusion tensor imaging," Human Brain Map., vol. 24, pp. 92-98, 2005.
6. M. Wiegell, D. Tuch, H. Larsson, and V. Wedeen, "Automatic segmentation of thalamic nuclei from diffusion tensor magnetic resonance imaging," NeuroImage, vol. 19, no. 2, pp. 391-401, 2003.
7. U. Ziyan, D. Tuch, and C.-F. Westin, "Segmentation of thalamic nuclei from DTI using spectral clustering," in Proc. Med. Image Comput. Comp. Assisted Intervention, 2006, pp. 807-814.
8. M. Kinoshita, K. Yamada, N. Hashimoto, A. Kato, S. Izumoto, T. Baba, M. Maruno, T. Nishimura, and T. Yoshimine, "Fiber tracking does not accurately estimate size of fiber bundles in pathological conditions: Initial neurosurgical experience using neuronavigation and subcortical white matter stimulation," NeuroImage, vol. 25, no. 2, pp. 424-429, 2005.
9. M. Rousson, C. Lenglet, and R. Deriche, "Level set and region based surface propagation for diffusion tensor MRI segmentation," in ECCV Workshops CVAMIA MMBIA, 2004, pp. 123-134.
10. Z. Wang and B. Vemuri, "Tensor field segmentation using region-based active contour model," in Eur. Conf. Comp. Vis., 2004, pp. 304-315.
11. L. Jonasson, P. Hagmann, C. Polio, X. Bresson, C. Wilson, R. Meuli, and J. Thiran, "A level set method for segmentation of the thalamus and its nuclei in DT-MRI," Signal Process., vol. 87, pp. 309-321, 2006.
12. S. P. Awate and J. C. Gee, "A fuzzy, nonparametric segmentation framework for DTI and MRI analysis," in Proc. Inf. Process. Med. Imag. (IPMl), 2007, pp. 296-307.
13. M. Wand and M. Jones, Kernel Smoothing. London, U.K.: Chapman & Hall, 1995.
14. R. Duda, P. Hart, and D. Stork, Pattern Classification. New York: Wiley, 2001.
15. J. Kim, J. W. Fisher, A. J. Yezzi, M. Cetin, and A. S. Willsky, "A non-parametric statistical method for image segmentation using information theory and curve evolution," IEEE Trans. Image Process., vol. 14, no. 10, pp. 1486-1502, Oct. 2005.
16. S. P. Awate, T. Tasdizen, and R. T. Whitaker, "Unsupervised texture segmentation with nonparametric neighborhood statistics," in Proc. Eur. Conf. Computer Vision (ECCV), 2006, vol. 2, pp. 494-507.
17. S. P. Awate, T. Tasdizen, N. L. Foster, and R. T. Whitaker, "Adaptive, nonparametric Markov modeling for unsupervised, MRI brain-tissue classification," Mid. Image Anal., vol. 10, no. 5, pp. 726-739, 2006.
18. B. Pelletier, "Kernel density estimation, on Riemannian manifolds," Slot. Prob. Lett., vol. 73, pp. 297-304, 2005.
19. V. Arsigny, P. Fillard, X. Pennec, and N. Ayache, "Fast and simple calculus on tensors in the Log-Euclidean framework," in Proc. Int. Conf. Med. Image Comput. Comp. Assisted Intervention, 2005, pp. 115-122.
20. V. Arsigny, P. Fillard, X. Pennec, and N. Ayache, "Geomettic means in a novel vector space structure on symmetric positive-definite matrices," SIAM J. Matrix Anal. Appl., vol. 29, no. 1, pp. 328-347, 2007.
21. J. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. Norwell, MA: Kluwer, 1981.
22. L. Zhukov, K. Museth, D. Breen, R. Whitaker, and A. Barr, "Level set modeling and segmentation of DT-MRI brain data," Electron. Imag., vol. 12, no. 1, pp. 125-133, 2003.
23. J. Sethian, Level Set Methods and Fast Marching Methods. Cambridge, U.K.: Cambridge Univ. Press, 1999.
24. S. Osher and R. Fedkiw, Level Set Methods and Dynamic Implicit Surfaces. New York: Springer, 2003.
25. C. Feddern, J. Weickert, and B. Burgeth, "Level-set methods for tensor valued images," in Proc. IEEE Workshop Variational, Geometric Level Set Methods Computer Vision, 2003, pp. 65-72.
26. L. Jonasson, X. Bresson, P. Hagmann, O. Cuisenaire, R. Meuli, and J. Thiran, "White matter fiber tract segmentation in DT-MRI using geometric flows," Med. Image Anal., vol. 9, pp. 223-236, 2005.
27. P. T. Fletcher and S. C. Joshi, "Principal geodesic analysis on symmetric spaces: Statistics of diffusion tensors," in ECCV Workshops CVAMIA MMBIA, 2004, pp. 87-98.
28. X. Pennec, P. Fillard, and N. Ayache, "A Riemannian framework for tensor computing," Int. J. Comput. Vision, vol. 66, no. 1, pp. 41-66, 2006.
29. X. Zhuang, Y. Huang, K. Palaniappan, and Y. Zhao, "Gaussian mixture density modeling, decomposition, and applications," IEEE Trans. Image Process., vol. 5, no. 9, pp. 1293-1302, Sep. 1996.
30. R. Coppi and P. Durso, "Fuzzy unsupervised classification of multivariate time trajectories with the shannon entropy regularization," Statist. Data Anal., vol. 50, pp. 1452-1477, 2005.
31. W.M. Wells, W. E. L. Crimson, R. Kikinis, and F. A. Jolesz, "Adaptive segmentation of MRI data," IEEE Trans. Med. Imag., vol. 15, no. 4, pp. 429-443, Aug. 1996.
32. D. Pham and J. Prince, "An adaptive fuzzy segmentation algorithm for three-dimensional magnetic resonance images," in Proc. Info. Proc. Med. Imag., 1999, pp. 140-153.
33. L. O'Donnell, E. Grimson, and C. F. Westin, "Interface detection in diffusion tensor MRI," in Proc. Med. Imag. Comput. Comp. Assist, Inter., 2004, pp. 360-367.
34. D. Tuch, "Q-ball imaging," Magn. Reson. Med., vol. 52, no. 6, pp. 1358-1372, 2004.
35. E. Parzen, "On. the estimation of a probability density function, and the mode," Ann. Math. Stats., vol. 33, pp. 1065-1076, 1962.
36. W. Boothby, An Introduction to Differentiable Manifolds and Riemannian Geometry. New York: Academic, 2002.
37. S. Z. Li, Markov Random Field Modeling in Computer Vision.. New York: Springer, 1995.
38. L. Zadeh, "Fuzzy sets," Inf. Control, vol. 8, pp. 338-353, 1965.
39. G. Klir and B. Yuan, Eds., Fuzzy sets, fuzzy logic and fuzzy system: Selected Papers by Lotfi A. Zadeh. Singapore: World Scientific, 1996.
40. A. Papoulis and S. U. Pillai, Probability, Random Variables, and Stochastic Processes, 4th ed. New York: McGraw-Hill, 2001.
41. S. S. Rao, Engineering Optimization, Theory and Practice.. New York: Wiley, 1996.
42. Y. Chow, S. Geman, and L. Wu, "Consistant cross-validated density estimation," Ann. Stat., vol. 11, no. 1, pp. 25-38, 1983.
43. J. S. Simonoff, Smoothing Methods in Statistics.. New York: Springer, 1996.
44. I. J. Good, "A nonparametric roughness penalty for probability densities," Nature (London), vol. 229, pp. 29-30, 1971.
45. I. J. Good and R. A. Gaskins, "Nonparametric roughness penalties for probability densities," Biometrika, vol. 58, no. 2, pp. 255-277, 1971.
46. R. P. W. Duin, "On the choice of smoothing parameters for parzen estimators of probability density functions," IEEE Trans. Comput., vol. C-25, no. 11, pp. 1175-1179, 1976.
47. D. W. Scott and L. E. Factor, "Monte carlo study of three data-based nonparametric probability density estimators," J. Amer. Statist. Assoc., vol. 76, no. 373, pp. 9-15, 1981.
48. S. Geman and C. R. Hwang, "Nonparametric maximum likelihood estimation by method of sieves," Ann. Statist, vol. 10, no. 2, pp. 401-414, 1982.
49. P. Hall, "Cross-validation in density estimation," Biometrika, vol. 69, no. 2, pp. 382-390, 1982.
50. A. K. Jain, Fundamentals of Digital Image Processing. Englewood Cliffs, NJ: Prentice-Hall, 1989.
51. H. Zhang, B. B. Avants, P. A. Yushkevich, J. H. Woo, S. Wang, L. F. McCluskey, L. B. Elman, E. R. Melhetn, and J. C. Gee, "High-dimensional spatial normalization of diffusion tensor images improves the detection of white matter differences: An example study using amyotrophic lateral sclerosis," IEEE Trans. Med. Imag., 2007, to be published.
52. D. L. Collins, A. P. Zijdenbos, V. Kollokian, J. G. Sled, N. J. Kabani, C. J. Holmes, and A. C. Evans, "Design and construction, of a realistic digital brain phantom," IEEE Trans. Med. Imag., vol. 17, no. 3, pp. 463-468, Jun. 1998.
53. K. V. Leemput, F. Maes, D. Vandermeulen, and P. Seutens, "Automated model-based tissue classification of MR images of the brain," IEEE Trans. Med Imag., vol. 18, no. 10, pp. 897-908, Oct. 1999.
54. H. Greenspan, A. Ruf, and J. Goldberger, "Constrained Gaussian mixture model framework for automatic segmentation of MR brain images," IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1233-1245, Sep. 2006.