Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus University Medical Center, Rotterdam, the Netherlands.
Neuroinformatics. 2016 Apr;14(2):201-19. doi: 10.1007/s12021-015-9287-0.
Digital reconstruction of neuronal cell morphology is an important step toward understanding the functionality of neuronal networks. Neurons are tree-like structures whose description depends critically on the junctions and terminations, collectively called critical points, making the correct localization and identification of these points a crucial task in the reconstruction process. Here we present a fully automatic method for the integrated detection and characterization of both types of critical points in fluorescence microscopy images of neurons. In view of the majority of our current studies, which are based on cultured neurons, we describe and evaluate the method for application to two-dimensional (2D) images. The method relies on directional filtering and angular profile analysis to extract essential features about the main streamlines at any location in an image, and employs fuzzy logic with carefully designed rules to reason about the feature values in order to make well-informed decisions about the presence of a critical point and its type. Experiments on simulated as well as real images of neurons demonstrate the detection performance of our method. A comparison with the output of two existing neuron reconstruction methods reveals that our method achieves substantially higher detection rates and could provide beneficial information to the reconstruction process.
神经元细胞形态的数字重建是理解神经元网络功能的重要步骤。神经元是树状结构，其描述取决于连接点和端点，统称为关键点，因此正确定位和识别这些点是重建过程中的关键任务。在这里，我们提出了一种用于荧光显微镜神经元图像中两种类型关键点的集成检测和特征描述的全自动方法。鉴于我们目前的大多数研究都是基于培养的神经元，我们将描述和评估该方法在二维（2D）图像中的应用。该方法依赖于方向滤波和角度轮廓分析来提取图像中任何位置的主要流线的基本特征，并使用具有精心设计规则的模糊逻辑来推理特征值，以便对关键点的存在及其类型做出明智的决策。对神经元的模拟和真实图像的实验证明了我们方法的检测性能。与两种现有神经元重建方法的输出结果进行比较表明，我们的方法可以实现更高的检测率，并为重建过程提供有益的信息。
