Image segmentation by three-level thresholding based on maximum fuzzy entropy and genetic algorithm
In the paper, a three-level thresholding method for image segmentation is presented, based on probability partition, fuzzy partition and entropy theory. A new fuzzy entropy has been defined through probability analysis. The image is divided into three parts, namely, dark, gray and white part, whose member functions of the fuzzy region are Z-function and ?-function and S-function, respectively, while the width and attribute of the fuzzy region can be determined by maximizing fuzzy entropy. The procedure for finding the optimal combination of all the fuzzy parameters is implemented by a genetic algorithm with appropriate coding method so as to avoid useless chromosomes. The experiment results show that the proposed method gives good performance.
image segmentation fuzzy entropy genetic algorithm probability partition