IMAGE-ANALYSIS OF NISSL-STAINED NEURONAL PERIKARYA IN THE PRIMARY VISUAL-CORTEX OF THE RAT - AUTOMATIC DETECTION AND SEGMENTATION OF NEURONAL PROFILES WITH NUCLEI AND NUCLEOLI
IMAGE-ANALYSIS OF NISSL-STAINED NEURONAL PERIKARYA IN THE PRIMARY VISUAL-CORTEX OF THE RAT - AUTOMATIC DETECTION AND SEGMENTATION OF NEURONAL PROFILES WITH NUCLEI AND NUCLEOLI
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DOI：
10.1111/j.1365-2818.1990.tb02970.x
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发表时间：
1990-03-01
影响因子：
2
通讯作者：
WERNER, L
中科院分区：
文献类型：
--
作者：
AHRENS, P;SCHLEICHER, A;WERNER, L
本文描述了尼氏染色脑切片中皮质神经元形态计量学特征的图像分析方法。它包括对细胞轮廓及其隔间：细胞质、核和核仁的自动检测。该算法被设计用于处理皮质核周的大的形态谱(例如核周的几何特性、细胞室的染色强度和核浆面积比)，包括锥体(高尔基体-I类)和非锥体(高尔基体-II类)神经元。细胞团被分离，非神经性结构(如胶质细胞、内皮细胞)以及通过神经细胞膜的切向、非核化切片被识别并排除在进一步分析之外，不需要交互程序。使用426个核糖化和非核糖化的大鼠初级视皮层不同类型神经元的轮廓来评估轮廓识别过程的性能。核糖化图谱的识别准确率为91%，非核糖化图谱的正确拒识率为90%。在自动分割和选择来自微观领域的核仁化神经元轮廓后，可以使用高功率光学显微镜测量包括几何、密度和纹理参数在内的大量定量形态特征。这使得可以对不同类型的神经元进行定量的形态特征描述。该程序是尼氏染色皮质神经元自动分类系统的第一部分。
An image analysing procedure for the morphometric characterization of cortical neurons in Nissl-stained brain sections is described. It consists of the automatic detection of cellular profiles and their compartments: cytoplasm, nucleus and nucleolus. The algorithm was designed to cope with the large morphological spectrum of cortical perikarya (e.g. geometrical properties of perikarya, staining intensities of cell compartments and nucleo-plasmic area-ratio) including pyramidal (Golgi-category I) and non-pyramidal (Golgi-category II) neurons. Clusters of cells were separated and non-neuronal structures (e.g. glia, endothelial cells) as well as tangential, non-nucleolated sections through neuronal perikarya recognized and excluded from further analysis without requiring interactive procedures. The performance of the profile recognition procedure was evaluated using 426 nucleolated and non-nucleolated profiles of different types of neurons in the primary visual cortex of the rat. Nucleolated profiles were recognized as such with a 91% accuracy, non-nucleolated profiles were rejected correctly in 90% of cases. After automatic segmentation and selection of nucleolated neuronal profiles from the microscopic field, a large set of quantitative morphological features including geometrical, densitometrical and textural parameters can be measured using high power light microscopy. This permits quantitative morphometric characterization of different neuronal types. This procedure is the first part of a system for the automatic classification of Nissl-stained cortical neurons.