A novel statistical image thresholding method

Classic statistical thresholding methods based on maximizing between-class variance and minimizing class variance fail to achieve satisfactory results when segmenting a kind of image, where variance discrepancy between the object and background classes is large. The reason is that they take only class variance sum of some form as criterions for threshold selection, but neglect discrepancy of the variances. In this paper, a novel criterion combining the above two factors is proposed to eliminate the described limitation for classic statistical approaches and improve segmentation performance. The proposed method determines the optimal threshold by minimizing the criterion. The method was compared with several classic thresholding methods on a variety of images including some NDT images and laser cladding images, and the experimental results show the effectiveness of the algorithm.