An image thresholding approach based on Gaussian mixture model

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Image thresholding is an important technique for partitioning the image into foreground and background in image processing and analysis. It is difficult for traditional thresholding methods to get satisfactory performance on the noisy and uneven grayscale images. In this paper, we propose an image thresholding approach based on Gaussian mixture model (GMM) to solve this problem. GMM assumes that image is a mixture of two unknown parameters’ Gaussian distributions, which corresponds to foreground and background, respectively. Based on this assumption, we adopt expectation maximization algorithm with a simple initialization strategy to estimate the statistical parameters and utilize Bayesian criteria to generate the binary map. Furthermore, we calculate the posterior probabilities in consideration of neighborhood effect to achieve good performance on noisy and uneven grayscale images. Experimental results conducted on the synthetic and real images demonstrate the effectiveness of the proposed method.