A technique of three-level thresholding based on probability partition and fuzzy 3-partition
Thresholding is a commonly used technique in image segmentation. Selecting the correct thresholds is a critical issue. In this paper, the relationship between a probability partition (PP) and a fuzzy c-partition (FP) in thresholding is given. This relationship and the entropy approach are used to derive a thresholding technique to select the best fuzzy c-partition. The measure of the selection quality is the compatibility between the FP and the PP generated by the problem. An entropy function defined by the PP and FP is used to measure the compatibility. A necessary condition of the entropy function arriving at a maximum is derived. Based on this condition, an efficient algorithm for three-level thresholding is deduced. Experiments to verify the efficiency of the proposed method and comparison to some existing techniques are also presented. The experiment results show that our proposed method gives the best performance in three-level thresholding using fuzzy c-partition.
thresholding probability partition fuzzy c-partition entropy image processing