Du Yuncheng, Budman Hector M, Duever Thomas A
1Chemical Engineering,Clarkson University,8 Clarkson Ave,Potsdam,NY 13699-5805,USA.
2Chemical Engineering,University of Waterloo,200 University Ave,Waterloo,ON N2L 3G1,Canada.
Microsc Microanal. 2017 Jun;23(3):569-583. doi: 10.1017/S1431927617000381. Epub 2017 Apr 3.
Accurate and fast quantitative analysis of living cells from fluorescence microscopy images is useful for evaluating experimental outcomes and cell culture protocols. An algorithm is developed in this work to automatically segment and distinguish apoptotic cells from normal cells. The algorithm involves three steps consisting of two segmentation steps and a classification step. The segmentation steps are: (i) a coarse segmentation, combining a range filter with a marching square method, is used as a prefiltering step to provide the approximate positions of cells within a two-dimensional matrix used to store cells' images and the count of the number of cells for a given image; and (ii) a fine segmentation step using the Active Contours Without Edges method is applied to the boundaries of cells identified in the coarse segmentation step. Although this basic two-step approach provides accurate edges when the cells in a given image are sparsely distributed, the occurrence of clusters of cells in high cell density samples requires further processing. Hence, a novel algorithm for clusters is developed to identify the edges of cells within clusters and to approximate their morphological features. Based on the segmentation results, a support vector machine classifier that uses three morphological features: the mean value of pixel intensities in the cellular regions, the variance of pixel intensities in the vicinity of cell boundaries, and the lengths of the boundaries, is developed for distinguishing apoptotic cells from normal cells. The algorithm is shown to be efficient in terms of computational time, quantitative analysis, and differentiation accuracy, as compared with the use of the active contours method without the proposed preliminary coarse segmentation step.
从荧光显微镜图像中对活细胞进行准确快速的定量分析，对于评估实验结果和细胞培养方案很有用。在这项工作中开发了一种算法，用于自动分割凋亡细胞与正常细胞并进行区分。该算法包括三个步骤，由两个分割步骤和一个分类步骤组成。分割步骤如下：（i）粗分割，将范围滤波器与行进正方形方法相结合，用作预滤波步骤，以提供用于存储细胞图像的二维矩阵内细胞的近似位置以及给定图像的细胞数量计数；（ii）使用无边缘主动轮廓方法的精细分割步骤应用于在粗分割步骤中识别出的细胞边界。虽然这种基本的两步方法在给定图像中的细胞稀疏分布时能提供准确的边缘，但在高细胞密度样本中细胞簇的出现需要进一步处理。因此，开发了一种用于细胞簇的新算法，以识别细胞簇内细胞的边缘并近似其形态特征。基于分割结果，开发了一种支持向量机分类器，它使用三个形态特征：细胞区域内像素强度的平均值、细胞边界附近像素强度的方差以及边界长度，用于区分凋亡细胞与正常细胞。与不使用所提出的初步粗分割步骤的主动轮廓方法相比，该算法在计算时间、定量分析和区分准确性方面都显示出高效性。
