Characteristic analysis of Otsu threshold and its applications

This paper proves that Otsu threshold is equal to the average of the mean levels of two classes partitioned by this threshold. Therefore, when the within-class variances of two classes are different, the threshold biases toward the class with larger variance. As a result, partial pixels belonging to this class will be misclassified into the other class with smaller variance. To address this problem and based on the analysis of Otsu threshold, this paper proposes an improved Otsu algorithm that constrains the search range of gray levels. Experimental results demonstrate the superiority of new algorithm compared with Otsu method.

Research highlights
► Otsu threshold is equal to the average of mean levels of two classes divided by it.
► Otsu threshold biases toward the class with larger variance.
► Otsu threshold is not equal to the threshold of the iterative method.
► The property of Otsu threshold indicates the search range of the optimal threshold.
► An improved Otsu method that constrains the search range of gray levels is proposed.