In this article, we present a new color image segmentation method, based on multilevel thresholding and data fusion techniques which aim at combining different data sources associated to the same color image in order to increase the information quality and to get a more reliable and accurate segmentation result. The proposed segmentation approach is conceptually different and explores a new strategy. In fact, instead of considering only one image for each application, our technique consists in combining many realizations of the same image, together, in order to increase the information quality and to get an optimal segmented image. For segmentation, we proceed in two steps. In the first step, we begin by identifying the most significant peaks of the histogram. For this purpose, an optimal multi-level thresholding is used based on the two-stage Otsu optimization approach. In the second step, the evidence theory is employed to merge several images represented in different color spaces, in order to get a final reliable and accurate segmentation result. The notion of mass functions, in the DempsterShafer (DS) evidence theory, is linked to the Gaussian distribution, and the final segmentation is achieved, on an input image, expressed in different color spaces, by using the DS combination rule and decision. The algorithm is demonstrated through the segmentation of medical color images. The classification accuracy of the proposed method is evaluated and a comparative study versus existing techniques is presented. The experiments were conducted on an extensive set of color images. Satisfactory segmentation results have been obtained showing the effectiveness and superiority of the proposed method.
analysis method based on homogeneity and color features is employed to determine the uniform regions. Finally, aIn this paper, entropy and between-class variance based region merging process is employed to ovoid averthresholding methods for color images segmentation are segmentation.studied. The maximization of the between-class variance Also, the Fuzzy C-means (FCM) [6], an unsupervised (MVI) and the entropy (ME) have been used as a criterion clustering algorithm, has been applied successfully to a functions to determine an optimal threshold to segment number of clustering problems. The FCM method of images into nearly homogenous regions. image segmentation identified the uniform region bySegmentation results from the two methods are validated minimizing the objective function for the partition of data and the segmentation sensitivity for the test data available set. However this method becomes very time-consuming is evaluated, and a comparative study between these due to the fact a large number of iterations are required
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