Stratégies de segmentation d'images multicomposantes par analyse d'histogrammes multidimensionnels : Application à des images couleur de coupes histologiques de pommes
Sié Ouattara · HAL (Le Centre pour la Communication Scientifique Directe) · 2009
Recent technical progresses supply sensors able to characterize a scene by multicomponent images. Most often, the segmentation of multicomponent images proceeds either through the analysis of their marginal histograms, ignoring the correlation between components, or by requantifying the components due to the difficulty of treating hudge n-dimensional (nD) histograms (n is the number of the image components). In a first step, we have developed a vectorial unsupervised and non parametric segmentation of multicomponent images (n ≥ 3), which is based on the hierarchical analysis of the compact nD histograms (an algorithmic data structure which diminishes the histogram size without losses). The modes of the histogram are obtained by labelling its connected components. The segmentation results are compared to those supplied by the classical K-means method, by using the criterions of Levine-Nazif, Zeboudj, Borsotti or Rosenberger. In a second step, to avoid the over-segmentation resulting from the diffuse character of the nD histograms, we propose to label their connected components in a fuzzy manner. The results are compared to those obtained by a requantification of the histogram. Thus we dispose of four segmentation strategies, and we compare their results on a set of natural as well as synthetic images. At last, this work is used to analyze histological cuts of apples in optical microscopy. The results show differences between three apples species, in relation with texture and firmness analyses.