Masseroli M, Bollea A, Forloni G
Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy.
Comput Methods Programs Biomed. 1993 Dec;41(2):89-99. doi: 10.1016/0169-2607(93)90068-v.
We describe a new image processing method for semiautomatic quantitative analysis of neuronal morphology. It has been developed in a specific image analysis environment (IBAS 2.0), but the algorithms and the methods can be employed elsewhere. The program is versatile and allows the analysis of histological preparations of different quality on the basis of different levels of evaluation and image extraction. Some significant algorithms have been implemented (i.e. one for multiple focus image acquisition and one for automatic cell body shape recognition and classification). A wide set of specific morphological parameters has been defined to allow a better mathematical characterization of neuronal morphology as regards both dendrite trees and cell bodies. Cell bodies' shapes can be classified automatically, defining different neuronal populations. This is done by evaluating the number of main dendrites and perikarya shapes through a multi-valued-decision-tree based method, tested on somatostatin-positive cells in mouse brain. The methods presented have been applied to analysis of neurons, but they can well be used for any quantitative morphological study of other cell populations.
我们描述了一种用于神经元形态半自动定量分析的新图像处理方法。它是在特定的图像分析环境（IBAS 2.0）中开发的，但算法和方法可在其他地方使用。该程序用途广泛，可基于不同的评估水平和图像提取对不同质量的组织学标本进行分析。已实现了一些重要算法（例如一种用于多焦点图像采集，一种用于自动识别和分类细胞体形状）。定义了一组广泛的特定形态学参数，以便在树突树和细胞体方面更好地对神经元形态进行数学表征。细胞体的形状可自动分类，从而定义不同的神经元群体。这是通过基于多值决策树的方法评估主要树突的数量和核周体形状来完成的，该方法已在小鼠脑内生长抑素阳性细胞上进行了测试。所介绍的方法已应用于神经元分析，但它们也可很好地用于其他细胞群体的任何定量形态学研究。
