Unsupervised Segmentation Method of Multicomponent Images based on Fuzzy Connectivity Analysis in the Multidimensional Histograms

DOI: 10.4236/eng.2011.33024   PDF   HTML     4,725 Downloads   8,687 Views   Citations


Image segmentation denotes a process for partitioning an image into distinct regions, it plays an important role in interpretation and decision making. A large variety of segmentation methods has been developed; among them, multidimensional histogram methods have been investigated but their implementation stays difficult due to the big size of histograms. We present an original method for segmenting n-D (where n is the number of components in image) images or multidimensional images in an unsupervised way using a fuzzy neighbourhood model. It is based on the hierarchical analysis of full n-D compact histograms integrating a fuzzy connected components labelling algorithm that we have realized in this work. Each peak of the histo- gram constitutes a class kernel, as soon as it encloses a number of pixels greater than or equal to a secondary arbitrary threshold knowing that a first threshold was set to define the degree of binary fuzzy similarity be- tween pixels. The use of a lossless compact n-D histogram allows a drastic reduction of the memory space necessary for coding it. As a consequence, the segmentation can be achieved without reducing the colors population of images in the classification step. It is shown that using n-D compact histograms, instead of 1-D and 2-D ones, leads to better segmentation results. Various images were segmented; the evaluation of the quality of segmentation in supervised and unsupervised of segmentation method proposed compare to the classification method k-means gives better results. It thus highlights the relevance of our approach, which can be used for solving many problems of segmentation.

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S. Ouattara, G. Loum and A. Clément, "Unsupervised Segmentation Method of Multicomponent Images based on Fuzzy Connectivity Analysis in the Multidimensional Histograms," Engineering, Vol. 3 No. 3, 2011, pp. 203-214. doi: 10.4236/eng.2011.33024.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] R. Furferi, “Colour Classification Method for Recycled Melange Fabrics,” Journal of Applied Sciences, Vol. 11, No. 2, 2011, pp. 236-246. doi:10.3923/jas.2011.236.246
[2] W. Yanqing, C. Deyun, S. Chaoxia and W. Peidong, “Vision-Based Road Detection by Monte Carlo Method,” Information Technology Journal, Vol. 9, 2010, pp. 481- 487.
[3] P. Vijayaprasad, M. N. Sulaiman, N. Mustapha and R. W. O. K. Rahmat, “Partial Fingerprint Recognition Using Support Vector Machine,” Information Technology Jour- nal, Vol. 9, No. 4, 2010, pp. 844-848. doi:10.3923/itj.2010.844.848
[4] L. Lixiong, W. Yuwei and W. Yuanquan, “A Novel Method for Segmentation of the Cardiac MR Images using Generalized DDGVF Snake Models with Shape Priors,” Information Technology Journal, Vol. 8, No. 4, 2009, pp. 486-494. doi:10.3923/itj.2009.486.494
[5] R. Gonzalez and O. Wintz, “Digital Image Processing,” 3rd Edition, Addison-Wesley Publishing Co., Massachu- setts, 1991.
[6] A. Clement and B. Vigouroux, “A Compact Histogram for the Analysis of Multicomponent Images,” Proceedings of 18th Conference GRETSI on Signal Processing, Vol. 1, 2001, pp. 305-307.
[7] S. Ouattara, A. Clément and F. Chapeau-Blondeau, “Fast Computation of Entropies and Mutual Information for Multispectral Images,” Proceeding of 4th International Conference on Informatics in Control, Automation and Robotics, Angers, Vol. 1, May 2007, pp. 195-199.
[8] S. Ouattara and A. Clement, “Labelling of Compact Mul- tidimensional Histograms for Analysis of Multicomponent Images,” Proceedings of the 21st Conference GRETSI on the Image Processing, Troyes, September 2007, pp. 85-88.
[9] L. Busin, N. Vandenbroucke, L. Macaire and J.-G. Postaire, “Color Space Selection for Unsupervised Color Image Segmentation by Histogram Multithresholding,” Proceedings of IEEE International Conference on Image Processing (ICIP’04), 2004, pp. 203-206.
[10] J. Hemming and T. Rath, “Computer-Vision-Based Weed Identification under Field Conditions Using Controlled Lighting,” Journal of Agricultural Engineering Research, Vol. 78, No. 3, 2001, pp. 233-243. doi:10.1006/jaer.2000.0639
[11] G. Xuan and P. Fisher, “Maximum Likelihood Clustering Method Based on Color Features,” Proceedings of the First International Conference on Color in Graphics and Image, Saint-Etienne, 2007, pp. 191-194.
[12] A. Clement and B. Vigouroux, “Unsupervised Segmen- tation of Scenes Containing Vegetation (Forsythia) and Soil by Hierarchical Analysis of Bidimensional Histograms,” Pattern Recognition Letters, Vol. 24, No. 12, August 2003, pp. 1951-1957. doi:10.1016/S0167-8655(03)00034-5
[13] W.-Y. Wei, Z.-M. Li, G.-C. Zhang and G.-Q. Zhang, “Novel Color Microscopic Image Segmentation with Simultaneous Uneven Illumination Estimation Based on PCA,” Information Technology Journal, Vol. 9, No. 8, 2010, pp. 1682-1685. doi:10.3923/itj.2010.1682.1685
[14] O. Lezoray and C. Charrier, “Color Image Segmentation using Morphological Clustering and Fusion with Automatic Scale Selection,” Pattern Recognition Letters, Vol. 30, No. 4, March 2009, pp. 397-406. doi:10.1016/j.patrec.2008.11.005
[15] O. Lezoray, “Unsupervised 2D Multiband Histogram Clustering and Region for Color Image Segmentation,” Proceedings of the 3rd IEEE International Symposium on Signal Processing and Information Technology, 2003, pp. 267-270.
[16] C. G. Looney, “Fuzzy Connectivity Clustering with Radial Basis Kernel Functions,” Fuzzy Sets and Systems, Vol. 160, No. 13, 2009, pp. 1868-1885. doi:10.1016/j.fss.2008.12.010
[17] O. Nempont, J. Atif, E. Angelini and I. Bloch, “A New Fuzzy Connectivity Measure for Fuzzy Sets: And Associated Fuzzy Attribute Openings,” Journal of Mathematical Imaging and Vision, Vol. 34, No. 2, 2009, pp. 107-136. doi:10.1007/s10851-009-0136-3
[18] D. Al-Bashish, M. Braik and S. Bani-Ahmad, “Detection and Classification of Leaf Diseases Using K-Means- Based Segmentation and Neural-Networks-Based Classification,” Information Technology Journal, Vol. 10, No. 2, 2011, pp. 267-275. doi:10.3923/itj.2011.267.275
[19] S. Dehuri, C. Mohapatra, A. Ghosh and R. Mall, “A comparative Study of Clustering Algorithms,” Information Technology Journal, Vol. 5, 2006, pp. 551-559. doi:10.3923/itj.2006.551.559
[20] H. Zhang, J. E. Fritts and S. A. Goldman, “Image Segmentation Evaluation: A Survey of Unsupervised Methods,” Computer Vision and Image Understanding, Vol. 110, No. 2, 2008, 260-280. doi:10.1016/j.cviu.2007.08.003
[21] O. Kubassova, M. Boesen and H. Bliddal, “General Framework for Unsupervised Evaluation of Quality of Segmentation Results,” 15th IEEE International Confe- rence on Image Processing (ICIP’08), 2008, pp. 3036- 3039.
[22] S. Chabrier, B. Emile, C. Rosenberger and H. Laurent, “Unsupervised Performance Evaluation of Image Segmen- tation,” EURASIP Journal on Applied Signal Processing, 2006, pp. 1-12. doi:10.1155/ASP/2006/96306
[23] C. P. Juan, E. S. David and F. R. Francisco, “Image Segmentation Based on Merging of Sub-Optimal Segmen- tations,” Pattern Recognition Letters, Vol. 27, No. 10, 2006, pp. 1105-1116.
[24] D. Dubois and H. Prade, “Fuzzy Sets and Systems — Theory and Applications,” Academic Press, New York, 1980.
[25] I. Bloch, “Fuzzy Spatial Relationship for Image Processing and Interpretation: A Review,” Image and Vision Computing,” Vol. 23, No. 2, February 2005, pp. 89-110. doi:10.1016/j.imavis.2004.06.013
[26] C. Demko and E. Zahzah, “Image Understanding Using Fuzzy Isomorphism of Fuzzy Structures,” Proceedings of International Joint Conference of the Fourth IEEE International Conference on Fuzzy Systems and the Second International Fuzzy Engineering Symposium, Yokohama, Vol. 3, 1995, pp. 1665-1672.
[27] B. M. Carvalho, G. T. Herman and T. Y. Kong, “Simultaneous Fuzzy Segmentation of Multiple Objects,” Discrete Applied Mathematics, Vol. 151, No. 1-3, 2005, pp. 55-77. doi:10.1016/j.dam.2005.02.031
[28] P. K. Saha, J. K. Udupa and D. Odhner, “Scale-Based Fuzzy Connected Image Segmentation: Theory, Algorithms, and Validation,” Computer Vision and Image Understanding, Vol. 77, No. 2, 2000, pp. 145-174. doi:10.1006/cviu.1999.0813
[29] J. Freixenet, X. Munoz, D. Raba, J. Marti and X. Cufi, “Yet Another Survey on Image Segmentation: Region and Boundary Information Integration,” Lecture Notes in Computer Science, Vol. 2352, 2002, pp. 21-25.
[30] D. Coquinand and Ph. Bolon, “Application of Baddeley’s Distance to Dissimilarity Measurement between Gray Scale Images,” Pattern Recognition Letters, Vol. 22, No. 14, December 2001, pp. 1483-1502. doi:10.1016/S0167-8655(01)00104-0
[31] L. Vinet, “Segmentation and Mapping of Areas of Stereoscopic Pairs of Images,” Ph.D. Dissertation, University of Paris IX Dauphine, Paris, 1991.
[32] D. P. Huttenlocher and W. J. Rucklidge, “A Multi-Reso- lution Technique for Comparing Images Using the Hausdorff Distance,” Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, New York, 1993, pp. 705-706. doi:10.1109/CVPR.1993.341019
[33] M. D. Levine and A. M. Nazif, “Dynamic Measurement of Computer Generated Image Segmentations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 7, No. 2, March 1985, pp. 155-164. doi:10.1109/TPAMI.1985.4767640
[34] M. Borsotti, P. Campadelli and R. Schettini, “Quantitative Evaluation of Color Image Segmentation Results,” Pattern Recognition Letter, Vol. 19, No. 8, 1998, pp. 741-747. doi:10.1016/S0167-8655(98)00052-X
[35] R. Zeboudj, “Automatic Thresholding, Contrast and Contours: The Pre-Treatment with the Image Analysis,” Ph.D. Dissertation, University of Saint Etienne, Saint Etienne, 1988.
[36] C. Rosenberger, “Adaptative Evaluation of Image Segmentation Results,” 18th International Conference on Pattern Recognition, Vol. 2, 2006, pp. 399-402. doi:10.1109/ICPR.2006.214

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