ABSTRACT In this work, we propose an original approach of semi-vectorial hybrid morphological segmentation for multicomponent images or multidimensional data by analyzing compact multidimensional histograms based on different orders. Its principle consists first of segment marginally each component of the multicomponent image into different numbers of classes fixed at K. The segmentation of each component of the image uses a scalar segmentation strategy by histogram analysis; we mainly count the methods by searching for peaks or modes of the histogram and those based on a multi-thresholding of the histogram. It is the latter that we have used in this paper, it relies particularly on the multi-thresholding method of OTSU. Then, in the case where i) each component of the image admits exactly K classes, K vector thresholds are constructed by an optimal pairing of which each component of the vector thresholds are those resulting from the marginal segmentations. In addition, the multidimensional compact histogram of the multicomponent image is computed and the attribute tuples or ‘colors’ of the histogram are ordered relative to the threshold vectors to produce (K + 1) intervals in the partial order giving rise to a segmentation of the multidimensional histogram into K classes. The remaining colors of the histogram are assigned to the closest class relative to their center of gravity. ii) In the contrary case, a vectorial spatial matching between the classes of the scalar components of the image is produced to obtain an over-segmentation, then an interclass fusion is performed to obtain a maximum of K classes. Indeed, the relevance of our segmentation method has been highlighted in relation to other methods, such as K-means, using unsupervised and supervised quantitative segmentation evaluation criteria. So the robustness of our method relatively to noise has been tested.
Cite this paper
Kouassi, A. , Ouattara, S. , Okaingni, J. , Vangah, W. and Clement, A. (2017) A Semi-Vectorial Hybrid Morphological Segmentation of Multicomponent Images Based on Multithreshold Analysis of Multidimensional Compact Histogram. Open Journal of Applied Sciences, 7, 597-610. doi: 10.4236/ojapps.2017.711043.
 Bitam Abdelmadjid, (2013) Multispectral Image Analysis and Segmentation: Application to MSG Images. PhD Thesis, Mouloud Mammeri University of Tizi-Ouzou, Tizi-Ouzou, Algerie.
 Ouattara, S. (2009) Multicomponent Image Segmentation Strategies by Analyzing Multidimensional Histograms: Application to Color Images of Histological Sections. PhD Thesis, University of Angers, Angers, France.
 Philip, S. and Cocoquevez, J.P. (1995) Image Analysis and Segmentation. Masson, Paris.
 Ouardia, A. (2011) Segmentation of Images by Bidimensional Histogram Thresholds. Memory of Magister, Mouloud Mammeri University of Tizi-Ouzou, Tizi-Ouzou, Algerie.
 Busin, L. (2003) Segmentation of Color Images by Analysis of Histogram Monodimensional Colors. Memory of DEA, University of Science and Technology of Lille and Central School of Lille, Lille, France.
 Herbulot, A. (2007) Non-Parametric Statistical Measurements for the Segmentation of Images and Videos and Minimization by Active Contours. Ph.D. Thesis, The University of Nice-Sophia Antipolis, Nice.
 El Merabet, Y. (2014) Segmentation of Color Images by Combination LPE-Regions/ LPE-Contours and Fusion of Regions. Application to the Segmentation of Roofs from Orthophotoplans. Ph.D. Thesis, University of Technology Belfort Montonberliard, Belfort.
 Ghandour, A. (2010) Segmentation of Color Images by Mathematical Morphology: Application to Images Microscopies. Ph.D. Thesis, The University of Toulouse, Toulouse.
 Madhava Raja, N.Sri, Rajinikanth, V. and Latha, K. (2014) Otsu Based Optimal Multilevel Image Thresholding Using Firefly Algorithm. Modelling and Simulation in Engineering, 2, Article ID: 794574. https://doi.org/10.1155/2014/794574
 Rajinikanth, V., Aashiha, J.P. and Atchay, A. (2014) Gray-Level Histogram based Multilevel Threshold Selection with Bat Algorithm. International Journal of Computer Applications (IJCA), 93, 1-8. https://doi.org/10.5120/16296-6035
 Arora, S., Acharya, J., Verma, A. and Panigrahi, P.K. (2008) Multilevel Thresholding for Image Segmentation through a Fast Statistical Recursive Algorithm. Pattern Recognition Letters, 29, 119-125. https://doi.org/10.1016/j.patrec.2007.09.005
 Campadelli, P., Medici, D. and Schettini, R. (1997) Color Image Segmentation Using Hopfield Networks. Image and Vision Computing, 15, 161-166.
 Tominaga, S. (1992) Color Classification of Natural Color Images. Color Research and Application, 17, 230-239. https://doi.org/10.1002/col.5080170405
 Ouattara, S., Loum, G.L. and Clément, A. (2011) Unsupervised Segmentation Method of Multicomponent Images Based on Fuzzy Connectivity Analysis in the Multidimensional Histograms. Engineering, 3, Article ID: 4141.
 Ouattara, S., Kouassi, A., Okaingni, J.C., Koffi, A., Loum, G. and Clement, A. (2016) A New Hybrid Order Approach to Morphological Color Image Processing Based on Reduced Order with Adaptive Absolute Reference. Engineering, 8, 633-645.
 Kouassi, A.F., Ouattara, S., Okaingni, J.-C., Koné, A., Vangah, W.J., Loum, G. and Clement, A. (2017) A New Vectorial Order Approach Based on the Classification of Tuples Attribute and Relative Absolute Adaptive Referent: Applications to Multicomponent Images. Journal of Software Engineering and Applications, 10, 546-563.
 Wang, X. and Bai, Y. (2016) The Global Minmax k-Means Algorithm. Springerplus, 5, 1665-1679. https://doi.org/10.1186/s40064-016-3329-4
 Clément, A. and Vigouroux, B. (2003) Unsupervised Segmentation of Scenes Containing Vegetation (Forsythia) and Soil by Hierarchical Analysis of Bi-Dimensional Histograms. Pattern Recognition Letters, 24, 1951-1957.