Optimal multi-thresholding using a hybrid optimization approach

The Otsu's method has been proven as an efficient method in image segmentation for bi-level thresholding. However, this method is computationally intensive when extended to multi-level thresholding. In this paper, we present a hybrid optimization scheme for multiple thresholding by the criteria of (1) Otsu's minimum within-group variance and (2) Gaussian function fitting. Four example images are used to test and illustrate the three different methods: the Otsu's method; the NM-PSO-Otsu method, which is the Otsu's method with Nelder-Mead simplex search and particle swarm optimization; the NM-PSO-curve method, which is Gaussian curve fitting by Nelder-Mead simplex search and particle swarm optimization. The experimental results show that the NM-PSO-Otsu could expedite the Otsu's method efficiently to a great extent in the case of multi-level thresholding, and that the NM-PSO-curve method could provide better effectiveness than the Otsu's method in the context of visualization, object size and image contrast.