Image Segmentation Using Two-dimensional Extension of Minimum Within-class Variance Criterion

Thresholding based on variance analysis of gray levels histogram is a very effective technology for image segmentation. However, its performance is limited in conventional forms. In this paper, a novel method based on two-dimensional extension of within-class variance is proposed to improve segmentation performance. The two-dimensional histogram of the original and local average image is projected to one-dimensional space firstly, and then the minimum within-class variance criterion is constructed for threshold selection. The effectiveness of the proposed method is demonstrated by using examples from the synthetic and real-word images.