Bibliography

[1] Canny, J. F., A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell., Vol. 8, pp. 679-698, 1986.

[2] Gonzalez, R. C. and Woods, R. E., Digital Image Processing, Addison-Wesley, Reading, MA, 1992.

[3] Kass, M., Witkin, A., and Terzopoulos, D., Snakes: Active contour models, Int. J. Computer Vision, Vol. 1, pp. 321-331, 1988.

[4] Cohen, L. D., On active contour models and balloons, CVGIP: Image Understanding, Vol. 53, pp. 211-218, 1991.

[5] Geman, D., Geman, S., Graffigne, C., and Dong, P., Boundary detection by constrained optimization, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 12, pp. 609-628, 1990.

[6] Hofmann, T., Puzicha, J., and Buhmann, J. M., Unsupervised texture segmentation in a deterministic annealing framework, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 20, pp. 803-818, 1998.

[7] Mumford, D. andShah, J., Optimal approximations by piecewise smooth functions and associated variational problems, Comm. Pure Appl. Math., Vol. 42, pp. 577-684, 1989.

[8] Ronfard, R., Region-based strategies for active contour models, Int. J. Comput. Vision, Vol. 13, pp. 1374-1387, 1994.

[9] Malladi, R., Sethian, J. A., and Vemuri, B. C., Shape modelling with front propagation: A level set approach, IEEE Trans. Patt. Anal. Mach. Intell., Vol. 17, pp. 158-175, 1995.

[10] Tsai, A., Yezzi, A., Wells, W., Tempany, C., Tucker, D., Fan, A., Grimson, W. E., and Willsky, A., A shape-based approach to the segmentation of medical imagery using level sets, IEEE Trans. Med. Imag., Vol. 22, pp. 137-154, 2003.

[11] Udupa, J. K. and Samarasekera, S., Fuzzy connectedness and object definition: Theory, algorithms and applications in image segmentation, Graph. Models Image Proc., Vol. 58, pp. 246-261,

1996.

[12] Carvalho, B. M., Gau, C. J., Herman, G. T., and Kong, T. Y., Algorithms for fuzzy segmentation, Pattern Anal. Appl., Vol. 2, pp. 73-81, 1999.

[13] Johnson, S. C., Hierarchical clustering schemes, Psychometrika, Vol. 32, pp. 241-254, 1967.

[14] Moghaddam, H. A. and Lerallut, J. F., Volume visualization of the heart using MRI4D cardiac images, J. Comput. Inform. Tech., Vol. 6, pp. 215228, 1998.

[15] Rice, B. L. and Udupa, J. K., Clutter-free volume rendering for magnetic resonance angiography using fuzzy connectedness, Int. J. Imag. Syst. Tech., Vol. 11, pp. 62-70, 2000.

[16] Saha, P. K., Udupa, J. K., and Odhner, D., Scale-based fuzzy connected image segmentation: Theory, algorithms and validation, Comput. Vision Image Understanding, Vol. 77, pp. 145-174, 2000.

[17] Udupa, J. K., Wei, L., Samarasekera, S., Miki, Y., van Buchem, M. A., and Grossman, R.I., Multiple sclerosis lesion quantification using fuzzy-connectedness principles, IEEE Trans. Med. Imag., Vol. 16, pp. 598-609,

1997.

[18] Rosenfeld, A., Fuzzy digital topology, Inform. Control, Vol. 40, pp. 76-87, 1979.

[19] Herman, G. T., Geometry of Digital Spaces, Birkhauser, Boston, MA,

1998.

[20] Dellepiane, S. G., Fontana, F., and Vernazza, G. L., Nonlinear image labeling for multivalued segmentation, IEEE Trans. Image Process., Vol. 5, pp. 429-446, 1996.

[21] Ahuja, N., Dot pattern processing using Voronoi neighborhoods. IEEE Trans. Pattern Anal. Mach. Intell., Vol. 3, pp. 336-343, 1982.

[22] Zahn, C. T., Graph-theoretic methods for detecting and describing Gestalt clusters, IEEE Trans. Comp., Vol. 1, pp. 68-86, 1971.

[23] Jain, A. K., Murty, M. N., and Flynn, P. J., Data clustering: A review, ACM Comput. Surveys, Vol. 31, pp. 264-323, 1999.

[24] Gower, J. C. and Ross, G. J. S., Minimum spanning trees and single linkage cluster analysis, Appl. Statist., Vol. 18, pp. 54-64, 1969.

[25] Pal, S. K. and Majumder, D. K. D., Fuzzy Mathematical Approach to Pattern Recognition, Wiley Eastern, L., New Delhi, India, 1986.

[26] Cormen, T. H., Leiserson, C. E., and Rivest, R. L., Introduction to Algorithms, MIT Press, Cambridge, MA, 1990.

[27] Udupa, J. K., Saha, P. K., Udupa, J. K., and Lotufo, R. A., Fuzzy connected object definition in images with respect to co-objects, In: Proc. SPIE, Bellingham, WA, Vol. 3661: Image Processing, Hanson, K. M., ed., pp. 236-245, 1999.

[28] Carvalho, B. M., Herman, G. T., and Kong, T. Y., Simultaneous fuzzy segmentation of multiple objects, In: Electronic Notes in Discrete Mathematics, Vol. 12, Del Lungo, A., Di Gesü, V., and Kuba, A., eds., Elsevier, Amsterdam, 2003. http://www.elsevier.com/gej-ng/31/29/24/71/23/59/endm12002.pdf.

[29] Herman, G. T. and Carvalho, B. M., Multiseeded segmentation using fuzzy conectedness, IEEE Trans. Pattern Anal. Mach. Intell., Vol. 23, pp. 460-474, 2001.

[30] Garduño, E., Vizualization and Extraction of Structural Components from Reconstructed Volumes, Ph.D. Thesis, Bioengineering Program, University of Pennsylvania, 2002.

[31] Pollak, I., Willsky, A. S., and Krim, H., Image segmentation and edge enhancement with stabilized inverse diffusion equations, IEEE Trans. Image Proc., Vol. 9, pp. 256-266, 2000.

[32] Koepfler, G., Lopez, C., and Morel, J.-M., A multiscale algorithm for image segmentation by variational method, SIAM J. Numer. Anal., Vol. 31, pp. 282-299, 1994.

[33] Petersen, D. P. and Middleton, D., Sampling and reconstruction of wave-number-limited functions in N-dimensional Euclidean spaces, Inform. and Control, Vol. 5, pp. 279-323, 1962.

[34] Carvalho, B. M., Garduno, E., and Herman, G. T., Multiseeded fuzzy segmentation on the face centered cubic grid, In: Advances in Pattern Recognition: Second International Conference, ICAPR 2001, Rio de Janeiro, Brazil, 2001. LNCS Vol. 2013, Singh, S., Murshed, N., and Kropatsch, W., eds., Springer-Verlag, pp. 339-348, 2001.

[35] Carvalho, B. M., Cone-Beam Helical CT Virtual Endoscopy: Reconstruction, Segmentation and Automatic Navigation, Ph.D. Thesis, Computer and Information Science Program, University of Pennsylvania, 2003.

[36] IBM, Visualization Data Explorer User's Guide, Version 3 Release 1 Modification 4. http://www.opendx.org/support.html.

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