Many researchers attempted to quantify dispersion states of microstructures. Several algorithms to analyze micrographs are proposed to evaluate indicators reflecting the dispersion states. However, the values of these indicators can correlate several performances of a given system, to some degree. This paper proposes a new indicator, the degree of dispersion ($\mathit{DoD}$), that assesses the dispersion state by a random walk simulation and a cloud overlap estimation. The novelty of the proposed method is summarized as follows: firstly, the $\mathit{DoD}$ possesses various features such as stability, effectivity, and flexibility; secondly, the $\mathit{DoD}$ considers inter- and intra-cluster dispersions naturally; and finally, the $\mathit{DoD}$ has an adjustable parameter that reflects different contexts of various applications. The computation time takes about 1–3 min for 600 by 600 pixels images. We believe that the $\mathit{DoD}$ can be used in various industrial applications such as films, batteries, colloids, and nanocomposites.

许多研究人员试图量化微结构的分散状态。提出了几种分析显微照片的算法，以评估反映分散状态的指标。但是，这些指标的值可以在一定程度上关联给定系统的几种性能。本文提出了一个新的指标，分散程度（$\mathit{国防部}$），通过随机游走仿真和云重叠估算来评估分散状态。该方法的新颖性归纳如下：首先，$\mathit{国防部}$具有稳定性，有效性和灵活性等各种特征；其次，$\mathit{国防部}$自然地考虑集群内部和集群内部的分散；最后，$\mathit{国防部}$具有可调整的参数，可以反映各种应用程序的不同上下文。对于600 x 600像素的图像，计算时间大约需要1-3分钟。我们认为$\mathit{国防部}$ 可用于各种工业应用，例如薄膜，电池，胶体和纳米复合材料。