Automatic Optimal Thresholding Using Generalized Fuzzy Entropies and Genetic Algorithm

The use of fuzzy entropy for image segmentation is one of the most popular methods, which is used today. In a classical fuzzy entropy, using a fuzzy complement with an equilibrium point of 0.5 is a limitation, which reduces the chances of obtaining an optimal result. We use generalized fuzzy entropy phrases in this paper, which uses fuzzy complements of Sugeno and Yager, and corresponds the equilibrium point to the m parameter (0<m<1), and increases the chance of finding the optimal threshold. So, we will have many pictures depending on the points of balance, and by the genetic algorithm, we choose the best decision among them. The effect of this method have considered in medical images to find the brain tumors. Results have shown that the use of generalized fuzzy entropy and the genetic algorithm can greatly be used to find the optimal threshold. Presented method is very effective for reducing the number of intensity levels. Problems may cause images with height amount of unwanted information which is saved to the expanse of subjective more important information.