Optimum Multilevel Image Thresholding Based on Tsallis Entropy Method with Bacterial Foraging Algorithm

Multilevel image thresholding is an important operation in many analyses which is used in many applications. Selecting correct thresholds is a critical issue. In this paper, Bacterial Foraging (BF) algorithm based on Tsallis objective function is presented for multilevel thresholding in image segmentation. Experiments to verify the efficiency of the proposed method and comparison to Genetic Algorithm (GA) is presented. The experiment results show that the proposed method gives the best performance in multilevel thresholding. The method is also computationally efficient, more stable and can be applied to a wide class of computer vision applications, such as character recognition, watermarking technique and segmentation of wide variety of medical images.