Fast optimal multimodal thresholding based on between-class variance using a mixture of Gamma distributions

Images segmentation is an important issue for many applications as pattern recognition and computer vision. Thresholding is an important and fast technique used in most applications. Gaussian Otsu´s method is a thresholding technique based on between class variance. Gamma distribution models data more than Gaussian distribution. In this paper, we developed a new formula using Otsu´s method for estimating the optimal threshold values based on gamma distribution. Our method applied on bimodal and multimodal images. Also it uses an iteratively rather than sequentially to decrease the number of operations. Further, using gamma distribution give satisfying thresholding results in low-high contrast images where modes are symmetric or non-symmetric. For our results, we compared it with the original Gaussian Otsu´s method.