| | This Article | |
| |
| |
| | Share | |
| |
| |
| | Bibliographic References | |
| |
| |
| | Add to: | |
| |
 Digg
 Furl
 Spurl
 Blink
 Simpy
 Google
 Del.icio.us
 Y!MyWeb
| |
| | Search | |
| |
| |
| | |
Multiseeded Segmentation Using Fuzzy Connectedness
May 2001 (vol. 23 no. 5)
pp. 460-474
Abstract—Fuzzy connectedness has been effectively used to segment out an object in a badly corrupted image. We generalize the approach by providing a definition which is shown to always determine a simultaneous segmentation of multiple objects. For any set of seed points, the segmentation is uniquely determined by the definition. An algorithm for finding this segmentation is presented and its output is illustrated. The algorithm is fast as compared to other segmentation algorithms in current use. We also report on an evaluation of the accuracy and robustness of the algorithm based on experiments in which several users were repeatedly asked to identify the seed points for the algorithm in a number of images.
[1] S.G. Dellepiane, F. Fontana, and G.L. Vernazza, “Nonlinear Image Labeling for Multivalued Segmentation,” IEEE Trans. Image Processing, vol. 5, pp. 429-446, 1996.
[2] J.K. Udupa, L. Wei, S. Samarasekera, Y. Miki, M.A. van Buchem, and R.I. Grossman, “Multiple Sclerosis Lesion Quantification Using Fuzzy-Connectedness Principles,” IEEE Trans. Medical Imaging, vol. 16, pp. 598-609, 1997.
[3] H.A. Moghaddam and J.F. Lerallut, “Volume Visualization of the Heart Using MRI 4D Cardiac Images,” J. Computer Information Techology, vol. 6, pp. 215-228, 1998.
[4] B.M. Carvalho, C.J. Gau, G.T. Herman, and T.Y. Kong, “Algorithms for Fuzzy Segmentation,” Pattern Analysis Applications, vol. 2, pp. 73-81, 1999.
[5] P.K. Saha, J.K. Udupa, and D. Odhner, “Scale-Based Fuzzy Connected Image Segmentation: Theory, Algorithms, and Validation,” Computer Vision Image Understanding, vol. 77, pp. 145-174, 2000.
[6] B.L. Rice and J.K. Udupa, “Clutter-Free Volume Rendering for Magnetic Resonance Angiography Using Fuzzy Connectedness,” Int'l J. Image Systems Technology, vol. 11, pp. 62-70, 2000.
[7] A. Rosenfeld, “Fuzzy Digital Topology,” Information and Control, vol. 40, pp. 76-87, 1979.
[8] S.C. Johnson, “Hierarchical Clustering Schemes,” Psychometrika, vol. 32, pp. 241-254, 1967.
[9] J.K. Udupa and S. Samarasekera, “Fuzzy Connectedness and Object Definition: Theory, Algorithms, and Applications in Image Segmentation,” Graphic Models Image Processing, vol. 58, pp. 246-261, 1996.
[10] G.T. Herman, Geometry of Digital Spaces. Boston: Birkhauser, 1998.
[11] C.T. Zahn, “Graph-Theoretic Methods for Detecting and Describing Gestalt Clusters,” IEEE Trans. Computer, vol. 1, pp. 68-86, 1971.
[12] N. Ahuja, “Dot Pattern Processing Using Voronoi Neighborhoods,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 3, 336-343, 1982.
[13] M.N. Murty, A.K. Jain, and P.J. Flynn, “Data Clustering: A Review,” ACM Computing Surveys, vol. 31, no. 3, pp. 264-323, 1999.
[14] J.C. Gower and G.J.S. Ross, “Minimum Spanning Trees and Single Linkage Cluster Analysis,” Application Statistics, vol. 18, pp. 54-64, 1969.
[15] S.K. Pal and D.K.D. Majumder, Fuzzy Mathematical Approach to Pattern Recognition. New Delhi, India: Wiley Eastern Ltd., 1986.
[16] T.H. Cormen,C.E. Leiserson, and R.L. Rivest,Introduction to Algorithms.Cambridge, Mass.: MIT Press/McGraw-Hill, 1990.
[17] J.K. Udupa, P.K. Saha, and R.A. Lotufo, “Fuzzy Connected Object Definition in Images with Respect to Co-Objects,” Proc. SPIE Image Processing, K.M. Hanson, ed., vol. 3661, pp. 236-245 1999.
[18] I. Pollak, A.S. Willsky, and H. Krim, “Image Segmentation and Edge Enhancement with Stabilized Inverse Diffusion Equations,” IEEE Trans. Image Processing, vol. 9, pp. 256-266, 2000.
[19] G. Koepfler, C. Lopez, and J.-M. Morel, “A Multiscale Algorithm for Image Segmentation by Variational Method,” SIAM J. Numerical Analysis, vol. 31, pp. 282-299, 1994.
[20] T. Hofmann, J. Puzicha, and J.M. Buhmann, “Unsupervised Texture Segmentation in a Deterministic Annealing Framework,” IEEE Trans. Pattern Analysis and Machince Intelligence, vol. 20, pp. 803-818, 1998.
[21] S. Geman and D. Geman, “Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images,” IEEE Trans. Pattern Analysis and Machince Intelligence, vol. 6, pp. 721-741, 1984.
[22] S.C. Zhu and A. Yuille, “Region Competition: Unifying Snakes, Region Growing and Bayes/MDL for Multiband Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 18, pp. 884-900, 1996.
[23] L.G. Shapiro and R.M. Haralick, “Decomposition of Two-Dimensional Shape by Graph-Theoretic Clustering,” IEEE Trans. Pattern Analysis and Machince Intelligence, vol. 1, pp. 10-20, 1979.
[24] J. Shi and J. Malik, “Normalized Cuts and Image Segmenation,” Proc. IEEE CS Conf. Computer Vision and Pattern Recognition, pp. 731-737, June 1997.
Index Terms:
Segmentation, fuzzy connectedness, feature extraction, algorithms, clustering.
Citation:
Gabor T. Herman, Bruno M. Carvalho, "Multiseeded Segmentation Using Fuzzy Connectedness," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 5, pp. 460-474, May 2001, doi:10.1109/34.922705
