References

1. M. W. Vannier, R. L. Butterfield, D. Jordan, W. A. Murphy, R. G. Levitt, and M. Gado. Multispectral analysis of magnetic resonance images. Radiology, l54:22l-224, l985.

2. G. Gerig, J. Martin, R. Kikinis, O. Kubler, M. Shenton, and F. A. Jolesz. Unsupervised tissue type segmentation of 3D dual-echo MR head data. Image Vision and Computing, 29:l00-l32, l985.

3. K. O. Lim and A. Pfefferbaum. Segmentation of MR brain images into cerebrospinal fluid and white and gray matter. J Comput Assist Tomogr, l3:588-593, l989.

4. M. I. Kohn, N. K. Tanna, G. T. Herman, S. M. Resnick, P. D. Mozley, R. E. Gur, A. Alavi, R. A. Zimmerman, and R. C. Gur. Analysis of brain and cerebrospinal fluid volumes with MR imaging. Radiology, l78:ll5-l22, l99l.

5. H. S. Choi, D. R. Hanynor, and Y. Kim. Partial volume tissue classification of multichannel magnetic resonance images — a mixel model. IEEE Trans Med Imag, l0:395-407, l99l.

6. H.E. Cline, W.E. Lorensen, R. Kikinis, and F. Jolesz. Three-dimensional segmentation of MR images of the head using probability and connectivity. J Comput Assist Tomogr, l4(6):l037-l045, l990.

7. Z. Liang, J.R. MacFall, and D.P. Harrington. Parameter estimation and tissue segmentation from multispectral MR images. IEEE Trans Med Imag, l3:44l-449, l994.

8. W.M. Wells, W.E.L. Grimson, R. Kikins, and F.A. Jolesz. Adaptive segmentation ofMRI data. IEEE Trans Med Imag, l5:429-442, l996.

9. J.C. Rajapakse, J.N. Giedd, and J.L. Rapoport. Statistical approach to segmentation of single-channel cerebral MR images. IEEE Trans Med Imag, l6:l76-l86, l997.

10. R. Momenan, D. Hommer, R. Rawlings, U. Rutimann, M. Kerich, and D. Rio. Intensity-adaptive seg-mentation of single-echo Tl-weighted magnetic resonance images. Hum Brain Map, 5:l94-205, l997.

11. Y. Wang, T. Adah, S. Kung, and Z. Szabo. Quantification and segmentation of brain tissues from MR images: A probabilistic neural network approach. IEEE Trans Imag Proc, 7:ll65-ll8l, l998.

12. A.F. Goldszal, C. Davatzikos, D.L. Pham, M.X.H. Yan, R.N. Bryan, and S.M. Resnick. An image processing system for qualitative and quantitative volumetric analysis of brain images. J Comput Assist Tomogr, 22(5):827-837, l998.

13. A.L. Reiss, J.G. Hennessey, M. Rubin, L. Beach, M.T. Abrams, I.S. Warsofsky, A.M.C. Liu, and J.M. Links. Reliability and validity of an algorithm for fuzzy tissue segmentation of MRI. J Comput Assist Tomogr, 22:471-479, 1998.

14. B.M. Dawant, A.P. Zijidenbos, and R.A. Margolin. Correction of intensity variations in MR images for computer-aided tissue classification. IEEE Trans Med Imag, 12:770-781, 1993.

15. C.R. Meyer, H.B. Peyton, and J. Pipe. Retrospective correction of intensity inhomogeneities in MRI. IEEE Trans Med Imag, 14:36-41, 1995.

16. B. Johnston, M.S. Atkins, B. Mackiewish, and M. Anderson. Segmentation of multiple sclerosis lesions in intensity corrected multispectral MRI. IEEE Trans Med Imag, 15:154-169, 1996.

17. T.N. Pappas. An adaptive clustering algorithm for image segmentation. IEEE Trans on Signal Processing, 40:901-914, 1992.

18. M.X.H. Yan, and J.N. Karp. An adaptive Bayesian approach to three-dimensional MR brain segmentation. In: Proceedings of XIVth International Conference on Information Processing in Medical Imaging, pp. 201-213, 1995.

19. R.A. Robb. A software system for interactive and quantitative analysis of biomedical images. In: Höhne KH, Fuchs H, Pizer SM, eds. 3D Imaging in Medicine. NATO ASI Series, Vol. F60, pp. 333-361, 1990.

20. J.C. Bezdek, L.O. Hall, and L.P. Clarke. Review of MR image segmentation techniques using pattern recognition. Med Phys, 20:1033-48, 1993.

21. A.P. Zijdenbos and B.M. Dawant. Brain segmentation and white matter lesion detection in MR images. Critical Reviews in Biomedical Engineering, 22:401-465, 1994.

22. L.P. Clarke, R.P. Velthuizen, M.A. Camacho, J.J. Heine, M. Vaidyanathan, L.O. Hall, R.W. Thatcher, and M.L. Silbiger. MRI segmentation: methods and applications. J Magn Res Imag, 13:343-368, 1995.

23. A.J. Worth, N. Makris, V.S. Caviness, and D.N. Kennedy. Neuroanatomical segmentation in MRI: Technological objectives. International Journal on Pattern Recognition and Artificial Intelligence, 11:1161-1187, 1997.

24. D.L. Pham, C. Xu, and J.L. Prince. A survey of current methods in medical image segmentation. To appear in Annual Review of Biomedical Engineering, vol. 2, 2000. Annual Reviews, Palo Alto, CA.

25. P.K. Sahoo, S. Soltani, and A.K.C. Wong. A survey of thresholding techniques. Computer Vision, Graphics, and Image Processing, 41:233-260, 1988.

26. S. Zucker. Region growing: Childhood and adolescence. Comput Graph Image Proc, 5:382-399, 1976.

27. R.J. Schalkoff. Pattern Recognition: Statistical, Structural and Neural Approaches. John Wiley & Sons, New York, 1992.

28. A.K. Jain and R.C. Dubes. Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs, NJ, 1988.

29. L.O. Hall, A.M. Bensaid, L.P. Clarke, R.P. Velthuizen, M.S. Silbiger, and J.C. Bezdek. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans Neural Networks, 3:672-682, 1992.

30. E. Gelenbe, Y. Feng, and K.R.R. Krishnan. Neural network methods for volumetric magnetic resonance imaging of the human brain. Proceedings of the IEEE, 84:1488-1496, 1996.

31. W.E. Reddick, J.O. Glass, E.N. Cook, T.D. Elkin, and R.J. Deaton. Automated segmentation and classification of multispectral magnetic resonance images of brain using artificail neural networks. IEEE Trans Med Imag, 16:911918, 1997.

32. D.F. Specht. Probabilistic neural networks. Neural Networks 3:109-118, 1990.

33. J.C. Dunn. A fuzzy relative of the ISODATA process and its use in detecting compact well-sparated clusters. Journal of Cybernetics, 3:32-57, 1973.

34. D.L. Pham and J.L. Prince. An adaptive fuzzy c-means algorithm for image segmentation in the presence of intensity inhomogeneities. Pattern Recognition Letters, 20:57-68, 1999.

35. J. Talairach, P. Tournoux. Co-planar stereotaxic atlas of the human brain. Thieme Medical, New York, 1988.

36. N.C. Andreasen R. Rajarethinam, T. Cizadlo, S. Arndt, V.W. Swayze, L.A. Fishman, D.S. O'Leary, J.C. Ehrhardt, and W.T.C. Yuh. Automatic atlas-based volume estimation of human brain regions from MR images. J Comput Assist Tomogr, 20:98-106, 1996.

37. J.L. Lancaster, L.H. Rainey, J.L. Summerlin, C.S. Freitas, P.T. Fox, A.C. Evans, A.W. Toga, and J.C. Mazziotta. Automated labeling of the human brain: A preliminary report on the development and evaluation of a forward-transform method. Hum Brain Map, 5:238-242, 1997.

38. D.L. Collins, C.J. Holmes, T.M. Peters, and A.C. Evans. Automatic 3-D model-based neuroanatomical segmentation. Hum Brain Map, 3:190-208, 1995.

39. C. Davatzikos. Spatial normalization of 3D images using deformable models. J Comput Assist Tomogr, 20(4):656-665, 1996.

40. G.E. Christensen, S.C. Joshi, and M.I. Miller. Volumetric transformation of brain anatomy. IEEE Trans Med Imag, 16(6):864-877, 1997.

41. S. Sandor and R. Leahy. Surface-based labeling of cortical anatomy using a deformable atlas. IEEE Trans Med Imag, 16:41-54, 1997.

42. W.L. Nowinski, R.N. Bryan, and R. Raghavan. The Electronic Clinical Brain Atlas on CD-ROM. Thieme Medical, New York, 1997.

43. M.E. Brandt, T.P. Bohan, L.A. Kramer, and J.M. Fletcher. Estimation of CSF, white and gray matter volumes in hydrocephalic children using fuzzy clustering of MR images. Computerized Medical Imaging and Graphics, 18:25-34, 1994.

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