Multilevel Image thresholding is an important preprocessing algorithm in computer vision applications nowadays. Since most common thresholding methods take the desired count of thresholds as input by the user, thresholding methods that automatically determines a suitable count of thresholds from the input image itself are advantageous. In this article, a novel thresholding method based on a dynamic programming algorithm and a modification of Minimum Error Thresholding (MET) criterion is thoroughly presented. An empirical statistical study is performed to pinpoint why this proposed method is superior. Moreover, an extended comparison between this proposed method and other state-of-the-art methods is performed on a comprehensive set of natural, satellite and medical test images. The numerical results show that the proposed MET-DP method takes much less time than traditional dynamic programming thresholding methods when the number of thresholds is high. The proposed method can detect a suitable count of thresholds for most of tested images of different types. However, traditional methods that take the count of thresholds as input produce thresholded images of higher structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) values than MET-DP. Source code can be found on https://w3id.org/met-dp/article1-code

翻译：多级图像阈值分割是当前计算机视觉应用中重要的预处理算法。由于大多数常用阈值分割方法需要用户输入期望的阈值数量，因此能够从输入图像中自动确定合适阈值数量的方法更具优势。本文详细提出了一种基于动态规划算法和改进的最小误差阈值分割(MET)准则的新型阈值分割方法。通过实证统计研究，精确阐明了该方法的优越性。此外，在涵盖自然图像、卫星图像和医学图像的综合测试集上，将该方法与现有最优方法进行了扩展比较。数值结果表明，当阈值数量较大时，所提出的MET-DP方法所需时间远少于传统动态规划阈值分割方法。该方法能够为大多数不同类型的测试图像检测到合适的阈值数量。然而，将阈值数量作为输入的传统方法在结构相似性指数(SSIM)和峰值信噪比(PSNR)指标上优于MET-DP方法。源代码可在https://w3id.org/met-dp/article1-code获取。