Automatic thresholding using a modified valley emphasis

Abstract

Otsu's method is one of the most well-known methods for automatic thresholding, which serves as an important algorithm category for image segmentation. However, it fails if the histogram is close to unimodal or has large intra-class variances. To alleviate this limitation, improved Otsu's methods such as the valley emphasis method and weighted object variances method have been proposed, which still yield non-optimal segmentation performance in some cases. In this study, a modified valley metric using second-order derivative is proposed to improve the Otsu's algorithm. Experiments are firstly conducted on five typical test images whose histograms are unimodal, multimodal or have large intra-class variances, and then expanded to a larger data set consisting of 22 cell images. The proposed algorithm is compared with original Otsu's method and existing improved algorithms. Four evaluation metrics including misclassification error, foreground recall, Dice similarity coefficient and Jaccard index are adopted to quantitatively measure the segmentation performance. Results show that the proposed algorithm achieves best segmentation results on both data sets quantitatively and qualitatively. The proposed algorithm adapts the Otsu's method to more image subtypes, indicating a wider application in automatic thresholding and image segmentation field.