Analysis of tubular structures in three-dimensional confocal images
Analysis of tubular structures in three-dimensional confocal images
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DOI：
10.1088/0954-898x/13/3/308
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发表时间：
2002-08-01
影响因子：
7.8
通讯作者：
van Pelt, J
中科院分区：
文献类型：
--
作者：
Streekstra, GJ;van Pelt, J
了解神经元的形态与功能之间的关系是理解神经元在大脑信息处理中所起作用的重要工具。特别是，树枝状乔木的直径和长度被认为是至关重要的形态学特征。因此，准确检测形态特征，如中心线位置和直径是建立这种关系的先决条件。共聚焦显微镜图像的低信噪比和点扩散函数的特性阻碍了对神经元形态的准确检测。PSF的大小和各向异性导致特征检测存在偏差和方向依赖。我们通过利用高斯图像导数进行特征检测来处理这些问题。高斯核提供了具有低噪声敏感性的图像导数估计。树突状树的管状神经元段的中心线位置和直径等感兴趣的特征可以通过计算并随后利用高斯图像导数来检测。对于直径测量，显微镜的PSF被纳入导数计算。在真实和模拟共聚焦图像上的结果表明，即使在真实的成像条件下，也可以准确地估计出中心线的位置和直径，并且没有偏置。
Knowledge about the relationship between morphology and the function of neurons is an important instrument in understanding the role that neurons play in information processing in the brain. In paricular, the diameter and length of segments in dendritic arborization are considered to be crucial morphological features. Consequently, accurate detection of morphological features such as centre line position and diameter is a prerequisite to establish this relationship.Accurate detection of neuron morphology from confocal microscope images is hampered by the low signal to noise ratio of the images and the properties of the microscope point spread function (PSF). The size and the anisotropy of the PSF causes feature detection to be biased and orientation dependent.We deal with these problems by utilizing Gaussian image derivatives for feature detection. Gaussian kernels provide for image derivative estimates with low noise sensitivity. Features of interest such as centre line positions and diameter in a tubular neuronal segment of a dendritic tree can be detected by calculating and subsequently utilizing Gaussian image derivatives. For diameter measurement the microscope PSF is incorporated into the derivative calculation.Results on real and simulated confocal images reveal that centre line position and diameter can be estimated accurately and are bias free even under realistic imaging conditions.