为了改善2维阈值分割性能,提高图像分割的效率,在传统2维Otsu阈值分割算法的基础上,提出了一种基于图像边缘信息的2维阈值分割方法。这种改进的方法保留了2维Otsu阈值分割算法分割结果准确的优点,并在此基础上充分利用图像的边缘信息,通过分析图像的边缘直方图和阈值的关系来得到最优分割阈值。仿真实验结果表明,该方法与传统2维分割算法相比,不仅计算简单,而且实时性好。

In this paper,a new two-dimensional(2D) thresholding method is proposed in order to improve the efficiency of image segmentation.Based on the characteristic of two-dimensional histogram of image and the requirement of segmentation,one of the two dimensions is the pixel's gray value and the other is its neighboring average gray value.The proposed method utilizes the important edge histogram of the image to segment it,while based on the traditional two-dimensional(2D) Otsu thresholding algorithm.According to the foreknowledge about the relationship of the edge pixel histogram and the threshold vector(s,t),the proposed method derives the optimal threshold vector(s_best,t_best),by looking for the valley value existing between two peaks in the edge histogram.Emulational experiments show that,compared with the traditional two-dimensional(2D) Otsu algorithm,the presented method reduces computation complexity greatly and reduces the running time of the algorithms,while retains the advantage of the traditional two-dimensional(2D) OTSU algorithm,such as nonparametric,unsupervised,high performing quality and so on.It can be seen from the emulational result of cellular images that both the improvement and real-time quality of the proposed method are valid.