Cesar R M, Costa L D
Cybernetic Vision Research Group, GII-IFSC-University of São Paulo, São Carlos, SP, Brazil.
J Neurosci Methods. 1999 Nov 15;93(2):121-31. doi: 10.1016/s0165-0270(99)00120-x.
This work introduces a new approach to the characterization of neural cells by means of semi-automated generation of dendrograms; data structures which describe the inherently hierarchical nature of neuronal arborizations. Dendrograms describe the branched structure of neurons in terms of the length, average thickness and bending energy of each of the dendritic segments and allow in a straightforward manner, the inclusion of additional measures. The bending energy quantifies the complexity of the shape and can be used to characterize the spatial coverage of the arborizations (the bending energy is an alternative for other complexity measures such as the fractal dimension). The new approach is based on the partitioning of the cell's outer contour as a function of the high curvature points followed by a syntactical analysis of the segmented contours. The semi-automated method is robust and is an improvement on the time consuming manual generation of the dendrograms. Several experimental results are included in this paper which illustrate and corroborate the effectiveness of the approach. The technique presented in this paper is limited to planar neurons but could be extended to a 3D approach.
这项工作介绍了一种通过半自动生成树状图来表征神经细胞的新方法；树状图是描述神经元分支结构内在层次性质的数据结构。树状图根据每个树突段的长度、平均厚度和弯曲能量来描述神经元的分支结构，并能直接纳入其他测量方法。弯曲能量量化了形状的复杂性，可用于表征分支结构的空间覆盖范围（弯曲能量是分形维数等其他复杂性测量方法的替代方法）。新方法基于根据高曲率点对细胞外轮廓进行划分，然后对分割后的轮廓进行句法分析。这种半自动方法很稳健，是对耗时的手动生成树状图方法的改进。本文包含了几个实验结果，这些结果说明了并证实了该方法的有效性。本文提出的技术仅限于平面神经元，但可以扩展到三维方法。
