Dempster-Shafer Evidence Theory for Image Segmentation: Application in Cells Images
In this paper we propose a new knowledge model using
the Dempster-Shafer-s evidence theory for image segmentation and
fusion. The proposed method is composed essentially of two steps.
First, mass distributions in Dempster-Shafer theory are obtained from
the membership degrees of each pixel covering the three image
components (R, G and B). Each membership-s degree is determined by
applying Fuzzy C-Means (FCM) clustering to the gray levels of the
three images. Second, the fusion process consists in defining three
discernment frames which are associated with the three images to be
fused, and then combining them to form a new frame of discernment.
The strategy used to define mass distributions in the combined
framework is discussed in detail. The proposed fusion method is
illustrated in the context of image segmentation. Experimental
investigations and comparative studies with the other previous methods
are carried out showing thus the robustness and superiority of the
proposed method in terms of image segmentation.
[1] A. W. C. Liew, S. H. Leung, et W. H. Lau, " Fuzzy image clustering
incorporating spatial continuity," IEE Procedings on Vision Image
Signal Processing, Vol. 14, no. 2, pp. 185-192, April 2000.
[2] Dempster, Arthur P, "A generalization of Bayesian inference," Journal
of the Royal Statistical Society, Series B, Vol. 30, pp. 205-247, 1968.
[3] E. Rignot, R. Chellappa, and P. Dubois, "Unsupervised segmentation
of polarimetric SAR data using the covariance matrix," IEEE
Transactions Geosciences and Remote Sensing, Vol. 30, pp. 697-705,
1992.
[4] H. D. Cheng, Y. H. Chen, "Fuzzy partition of two dimensional
histogram and its application to thresholding," Pattern Recognition,
Vol. 32, pp. 825-843, 1999.
[5] H. D. Cheng, X. H. Jiang and Jingli Wang, "Color image segmentation
based on homogram thresholding and region merging," Pattern
Recognition, Vol. 25, pp. 373-393, 2002.
[6] H. D. Cheng, X. H. Jiang, Ying Sun, Jingli Wang, "Color image
segmentation: advances and prospects," Pattern Recognition to appear.
[7] I. Block, H. Maitre, "Fusion of image information under imprecision,
in: B. Bouchon-Meunier (Ed.), Aggregation and fusion of imperfect
information, series studies in fuzziness," Physica Verlag, Springer, pp.
189-213, 1997.
[8] J. C. Dunn, "A fuzzy relative of the Isodata process and its use in
detecting compact well-separated clusters," Journal of Cybernetics,
Vol. 3, pp. 32-57, 1974.
[9] J. Basak and D. Mahata, "A Connectionist Model for Corner Detection
in Binary and Gray Images," IEEE Transactions on neuronal networks,
vol. 11, no.5, September 2000.
[10] K. Raghu, J. M. Keller, "A possibilistic approach to clustering," IEEE
Transactions on Fuzzy Systems, Vol. 1, no. 2, 1993.
[11] L. O. Hall, A. M. Bensaid, L. Clarke, R. P. Velthuizen, M. Silbiger,
and J.C. Bezdek, " A comparison of neural network and fuzzy
clustering techniques in segmenting magnetic resonance images of the
brain," IEEE Transactions Neural Networks, Vol. 3, pp. 672-681,
1992.
[12] Lotfi A. Zadeh, "Fuzzy sets," Information and Control, Vol. 8, pp. 338-
353, 1965.
[13] Lotfi A. Zadeh, "Fuzzy Sets a Basis for a theory of Possibility," Fuzzy
sets and systems, Vol. 1, pp. 3-28, 1978.
[14] M. C. Shin, D. B. Goldgof, K. W. Bowyer et S. Nikiforou, "
Comparaison of Edge Detection Algorithms Using a Structure from
Motion Task," IEEE Transactions on systems, Man, and cybernetics
(SMC 01)-Part B: cybernetics, Vol. 31, no. 4, august 2001.
[15] P. K. Sahoo, S. Soltani and A.K.C Wong, "A survey of thresholding
techniques," Comput, Vision Graphics Image Process. Vol. 41, pp.
233-260, 1988.
[16] P. Vannoorenberche, O. Colot and D. de Brucq, "Color image
segmentation using dempster-shafer-s theory," Proc. ICIP-99, pp. 300-
304, October 1999.
[17] Shafer, Glenn, "A Mathematical Theory of Evidence," Princeton
University Press, 1976.
[18] W. Chumsamrong, P. Thitimajshima, and Y. Rangsanseri, "Synthetic
aperture radar (SAR) image segmentation using a new modified fuzzy
c-means algorithm," Proceedings of Geoscience and Remote Sensing
Symposium, Vol. 2, PP. 624-626, 2000.
[19] Y. Yang, Ch. Zheng and P. Lin, "Fuzzy C-means clustering algorithm
with a novel penalty term for image segmentation," Opto-Electronics
Rev., Vol. 13, no. 4, 2005.
[20] Y. I. Ohta, T. Kanade, T. Sakai, "Color information for region
segmentation," Comput. Graph. and Imag. Proc., Vol. 13, pp. 222-241,
1980.
[1] A. W. C. Liew, S. H. Leung, et W. H. Lau, " Fuzzy image clustering
incorporating spatial continuity," IEE Procedings on Vision Image
Signal Processing, Vol. 14, no. 2, pp. 185-192, April 2000.
[2] Dempster, Arthur P, "A generalization of Bayesian inference," Journal
of the Royal Statistical Society, Series B, Vol. 30, pp. 205-247, 1968.
[3] E. Rignot, R. Chellappa, and P. Dubois, "Unsupervised segmentation
of polarimetric SAR data using the covariance matrix," IEEE
Transactions Geosciences and Remote Sensing, Vol. 30, pp. 697-705,
1992.
[4] H. D. Cheng, Y. H. Chen, "Fuzzy partition of two dimensional
histogram and its application to thresholding," Pattern Recognition,
Vol. 32, pp. 825-843, 1999.
[5] H. D. Cheng, X. H. Jiang and Jingli Wang, "Color image segmentation
based on homogram thresholding and region merging," Pattern
Recognition, Vol. 25, pp. 373-393, 2002.
[6] H. D. Cheng, X. H. Jiang, Ying Sun, Jingli Wang, "Color image
segmentation: advances and prospects," Pattern Recognition to appear.
[7] I. Block, H. Maitre, "Fusion of image information under imprecision,
in: B. Bouchon-Meunier (Ed.), Aggregation and fusion of imperfect
information, series studies in fuzziness," Physica Verlag, Springer, pp.
189-213, 1997.
[8] J. C. Dunn, "A fuzzy relative of the Isodata process and its use in
detecting compact well-separated clusters," Journal of Cybernetics,
Vol. 3, pp. 32-57, 1974.
[9] J. Basak and D. Mahata, "A Connectionist Model for Corner Detection
in Binary and Gray Images," IEEE Transactions on neuronal networks,
vol. 11, no.5, September 2000.
[10] K. Raghu, J. M. Keller, "A possibilistic approach to clustering," IEEE
Transactions on Fuzzy Systems, Vol. 1, no. 2, 1993.
[11] L. O. Hall, A. M. Bensaid, L. Clarke, R. P. Velthuizen, M. Silbiger,
and J.C. Bezdek, " A comparison of neural network and fuzzy
clustering techniques in segmenting magnetic resonance images of the
brain," IEEE Transactions Neural Networks, Vol. 3, pp. 672-681,
1992.
[12] Lotfi A. Zadeh, "Fuzzy sets," Information and Control, Vol. 8, pp. 338-
353, 1965.
[13] Lotfi A. Zadeh, "Fuzzy Sets a Basis for a theory of Possibility," Fuzzy
sets and systems, Vol. 1, pp. 3-28, 1978.
[14] M. C. Shin, D. B. Goldgof, K. W. Bowyer et S. Nikiforou, "
Comparaison of Edge Detection Algorithms Using a Structure from
Motion Task," IEEE Transactions on systems, Man, and cybernetics
(SMC 01)-Part B: cybernetics, Vol. 31, no. 4, august 2001.
[15] P. K. Sahoo, S. Soltani and A.K.C Wong, "A survey of thresholding
techniques," Comput, Vision Graphics Image Process. Vol. 41, pp.
233-260, 1988.
[16] P. Vannoorenberche, O. Colot and D. de Brucq, "Color image
segmentation using dempster-shafer-s theory," Proc. ICIP-99, pp. 300-
304, October 1999.
[17] Shafer, Glenn, "A Mathematical Theory of Evidence," Princeton
University Press, 1976.
[18] W. Chumsamrong, P. Thitimajshima, and Y. Rangsanseri, "Synthetic
aperture radar (SAR) image segmentation using a new modified fuzzy
c-means algorithm," Proceedings of Geoscience and Remote Sensing
Symposium, Vol. 2, PP. 624-626, 2000.
[19] Y. Yang, Ch. Zheng and P. Lin, "Fuzzy C-means clustering algorithm
with a novel penalty term for image segmentation," Opto-Electronics
Rev., Vol. 13, no. 4, 2005.
[20] Y. I. Ohta, T. Kanade, T. Sakai, "Color information for region
segmentation," Comput. Graph. and Imag. Proc., Vol. 13, pp. 222-241,
1980.
@article{"International Journal of Information, Control and Computer Sciences:57394", author = "S. Ben Chaabane and M. Sayadi and F. Fnaiech and E. Brassart", title = "Dempster-Shafer Evidence Theory for Image Segmentation: Application in Cells Images", abstract = "In this paper we propose a new knowledge model using
the Dempster-Shafer-s evidence theory for image segmentation and
fusion. The proposed method is composed essentially of two steps.
First, mass distributions in Dempster-Shafer theory are obtained from
the membership degrees of each pixel covering the three image
components (R, G and B). Each membership-s degree is determined by
applying Fuzzy C-Means (FCM) clustering to the gray levels of the
three images. Second, the fusion process consists in defining three
discernment frames which are associated with the three images to be
fused, and then combining them to form a new frame of discernment.
The strategy used to define mass distributions in the combined
framework is discussed in detail. The proposed fusion method is
illustrated in the context of image segmentation. Experimental
investigations and comparative studies with the other previous methods
are carried out showing thus the robustness and superiority of the
proposed method in terms of image segmentation.", keywords = "Fuzzy C-means, Color image, data fusion,Dempster-Shafer's evidence theory", volume = "3", number = "11", pages = "2636-7", }
