A hierarchical multilevel thresholding method for edge information extraction using fuzzy entropy
Image thresholding is the fundamental procedure in image processing. Meanwhile, edge information is a very useful and important image representation. A hierarchical multilevel thresholding method for edge information extraction using fuzzy entropy is presented in this paper. To realize multilevel thresholding fast and effectively, a tree structure is used to express the histogram hierarchy of an image. In each level of the tree structure, the image is segmented by three-level thresholding algorithm based on the maximum fuzzy entropy principle. In theory, the histogram hierarchy can be combined arbitrarily with multilevel thresholding. In order to evaluate the edge information extraction performance of multilevel thresholding methods, an edge similarity function is developed for according to the edge matching metric. Several images are employed to calculate their edge similarity coefficients. Experiments show that the proposed edge similarity coefficient is a valid one to measure the similarity between two image edge maps and it avoids the process effectively to obtain truth edge maps of images which can be realized only by labor statistics. To evaluate the performance of the proposed multilevel thresholding algorithm, the thresholded values of test images are calculated and compared using the proposed method, the Otsu and Kapur method, as well as edge similarity coefficients with the original images. The experimental results show that the proposed method spends less time to reach the better thresholds in edge similarity than existing multilevel thresholding methods.
multilevel thresholding edge information histogram hierarchy maximum fuzzy entropy principle edge similarity