Laboratory of Cell Biophysics, EPF Lausanne, Lausanne, Switzerland.
J Microsc. 2012 Feb;245(2):161-70. doi: 10.1111/j.1365-2818.2011.03558.x. Epub 2011 Oct 17.
Accurate extraction of cell outlines from microscopy images is essential for analysing the dynamics of migrating cells. Phase-contrast microscopy is one of the most common and convenient imaging modalities for observing cell motility because it does not require exogenous labelling and uses only moderate light levels with generally negligible phototoxicity effects. Automatic extraction and tracking of high-resolution cell outlines from phase-contrast images, however, is difficult due to complex and non-uniform edge intensity. We present a novel image-processing method based on refined level-set segmentation for accurate extraction of cell outlines from high-resolution phase-contrast images. The algorithm is validated on synthetic images of defined noise levels and applied to real image sequences of polarizing and persistently migrating keratocyte cells. We demonstrate that the algorithm is able to reliably reveal fine features in the cell edge dynamics.
从显微镜图像中准确提取细胞轮廓对于分析迁移细胞的动力学至关重要。相差显微镜是观察细胞运动最常用和最方便的成像方式之一，因为它不需要外源标记，并且只使用适度的光照水平，通常具有可忽略的光毒性作用。然而，由于边缘强度复杂且不均匀，从相差图像中自动提取和跟踪高分辨率的细胞轮廓是困难的。我们提出了一种基于改进水平集分割的新图像处理方法，用于从高分辨率相差图像中准确提取细胞轮廓。该算法在定义噪声水平的合成图像上进行了验证，并应用于偏光和持续迁移的角膜细胞的真实图像序列。我们证明，该算法能够可靠地揭示细胞边缘动态中的细微特征。
