Two-dimensional extension of variance-based thresholding for image segmentation

Variance-based thresholding method is a very effective technology for image segmentation. However, its performance is limited in traditional one-dimensional and two-dimensional scheme. In this paper, a novel two-dimensional variance thresholding scheme to improve image segmentation performance is proposed. The two-dimensional histogram of the original and local average image is projected to one-dimensional space in the proposed scheme firstly, and then the variance-based criterion is constructed for threshold selection. The experimental results on bi-level and multilevel thresholding for synthetic and real-world images demonstrate the success of the proposed image thresholding scheme, as compared with the Otsu method, the two-dimensional Otsu method and the minimum class variance thresholding method.