Methodist Hospital Research Institute, Radiology Department, Houston, TX 77030, USA.
J Neurosci Methods. 2010 Jul 15;190(2):299-309. doi: 10.1016/j.jneumeth.2010.05.012. Epub 2010 May 24.
High content neuron image processing is considered as an important method for quantitative neurobiological studies. The main goal of analysis in this paper is to provide automatic image processing approaches to process neuron images for studying neuron mechanism in high content screening. In the nuclei channel, all nuclei are segmented and detected by applying the gradient vector field based watershed. Then the neuronal nuclei are selected based on the soma region detected in neurite channel. In neurite images, we propose a novel neurite centerline extraction approach using the improved line-pixel detection technique. The proposed neurite tracing method can detect the curvilinear structure more accurately compared with the current existing methods. An interface called NeuriteIQ based on the proposed algorithms is developed finally for better application in high content screening.
高内涵神经元图像处理被认为是定量神经生物学研究的重要方法。本文分析的主要目标是提供自动图像处理方法，以处理神经元图像，用于高通量筛选中研究神经元机制。在细胞核通道中，应用基于梯度向量场的分水岭算法分割和检测所有细胞核。然后根据神经突通道中检测到的胞体区域选择神经元核。在神经突图像中，我们提出了一种新颖的神经突中心线提取方法，使用改进的线-像素检测技术。与现有方法相比，所提出的神经突跟踪方法可以更准确地检测曲线结构。最后，开发了一个名为 NeuriteIQ 的界面，基于所提出的算法，以便更好地应用于高通量筛选。
