技术报告-从2D神经元图像中自动提取轮廓
Technical Report - Automatic Contour Extraction from 2D Neuron Images
摘要：
这项工作描述了一种新的方法，自动轮廓提取从2D图像的3D神经元（如相机lucida图像和其他类型的2D显微镜）。大多数基于轮廓的形状分析方法不能用于表征这样的细胞，因为神经元过程之间的重叠。拟议的框架是专门针对的问题，即使在存在多个重叠的轮廓。首先，对输入图像进行预处理，以获得具有一个像素宽的分支的8连接骨架，以及一组关键区域（即，分叉和交叉）。接下来，对于每个子树，跟踪阶段迭代地标记分支的所有有效像素，直到临界区域，在该临界区域中，它确定合适的方向以继续进行。最后，标记的骨架段之后，以产生分析下的神经元形状的参数轮廓。所报告的系统成功地测试了几个图像和一组三个神经元图像的结果在这里呈现，每个属于不同的类，即α，δ和β神经节细胞，共包含34个交叉。算法成功地跨越了所有这些重叠。该方法也被发现表现出鲁棒性，即使是图像与密切的平行段。所提出的方法是鲁棒的，并且可以以有效的方式实现。这种方法的引入应该为更系统地应用基于轮廓的形状分析方法在神经元形态学中铺平道路。
英文摘要：
This work describes a novel methodology for automatic contour extraction from 2D images of 3D neurons (e.g. camera lucida images and other types of 2D microscopy). Most contour-based shape analysis methods can not be used to characterize such cells because of overlaps between neuronal processes. The proposed framework is specifically aimed at the problem of contour following even in presence of multiple overlaps. First, the input image is preprocessed in order to obtain an 8-connected skeleton with one-pixel-wide branches, as well as a set of critical regions (i.e., bifurcations and crossings). Next, for each subtree, the tracking stage iteratively labels all valid pixel of branches, up to a critical region, where it determines the suitable direction to proceed. Finally, the labeled skeleton segments are followed in order to yield the parametric contour of the neuronal shape under analysis. The reported system was successfully tested with respect to several images and the results from a set of three neuron images are presented here, each pertaining to a different class, i.e. alpha, delta and epsilon ganglion cells, containing a total of 34 crossings. The algorithms successfully got across all these overlaps. The method has also been found to exhibit robustness even for images with close parallel segments. The proposed method is robust and may be implemented in an efficient manner. The introduction of this approach should pave the way for more systematic application of contour-based shape analysis methods in neuronal morphology.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
