Grishin Igor, Thomson Kevin, Migliorini Francesca, Sloan James J
University of Waterloo, Waterloo, Ontario, Canada.
Appl Opt. 2012 Feb 10;51(5):610-20. doi: 10.1364/AO.51.000610.
We report a new method for automated identification and measurement of primary particles within soot aggregates as well as the sizes of the aggregates and discuss its application to high-resolution transmission electron microscope (TEM) images of the aggregates. The image processing algorithm is based on an optimized Hough transform, applied to the external border of the aggregate. This achieves a significant data reduction by decomposing the particle border into fragments, which are assumed to be spheres in the present application, consistent with the known morphology of soot aggregates. Unlike traditional techniques, which are ultimately reliant on manual (human) measurement of a small sample of primary particles from a subset of aggregates, this method gives a direct measurement of the sizes of the aggregates and the size distributions of the primary particles of which they are composed. The current version of the algorithm allows processing of high-resolution TEM images by a conventional laptop computer at a rate of 1-2 ms per aggregate. The results were validated by comparison with manual image processing, and excellent agreement was found.
我们报告了一种用于自动识别和测量烟灰聚集体内初级颗粒以及聚集体尺寸的新方法，并讨论了其在聚集体的高分辨率透射电子显微镜（TEM）图像中的应用。图像处理算法基于优化的霍夫变换，应用于聚集体的外部边界。通过将颗粒边界分解成片段，实现了显著的数据减少，在本应用中这些片段被假定为球体，这与烟灰聚集体的已知形态一致。与传统技术不同，传统技术最终依赖于从聚集体子集中手动（人工）测量一小部分初级颗粒，而该方法直接测量聚集体的尺寸及其组成的初级颗粒的尺寸分布。该算法的当前版本允许用传统笔记本电脑以每个聚集体1 - 2毫秒的速度处理高分辨率TEM图像。通过与手动图像处理进行比较验证了结果，发现二者具有极佳的一致性。
