Centre for Image Analysis, Swedish University of Agricultural Sciences and Uppsala University, Uppsala, Sweden.
J Microsc. 2012 Feb;245(2):140-7. doi: 10.1111/j.1365-2818.2011.03556.x. Epub 2011 Oct 4.
In this paper, we present an automatic segmentation method that detects virus particles of various shapes in transmission electron microscopy images. The method is based on a statistical analysis of local neighbourhoods of all the pixels in the image followed by an object width discrimination and finally, for elongated objects, a border refinement step. It requires only one input parameter, the approximate width of the virus particles searched for. The proposed method is evaluated on a large number of viruses. It successfully segments viruses regardless of shape, from polyhedral to highly pleomorphic.
本文提出了一种自动分割方法,用于检测透射电子显微镜图像中各种形状的病毒颗粒。该方法基于对图像中所有像素的局部邻域进行统计分析,然后进行对象宽度判别,最后对细长对象进行边界细化。它只需要一个输入参数,即搜索的病毒颗粒的近似宽度。所提出的方法在大量病毒上进行了评估。它可以成功分割形状各异的病毒,包括多面体和高度多形性的病毒。