Neuron recognition by parallel Potts segmentation.
Neuron recognition by parallel Potts segmentation.
复制标题
通过并行 Potts 分割进行神经元识别。
DOI：
10.1073/pnas.0230490100
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发表时间：
2003
期刊：
影响因子：
--
通讯作者：
Stanley,HE
中科院分区：
文献类型：
--
作者：
Peng,S;Urbanc,B;Cruz,L;Hyman,BT;Stanley,HE
识别大脑皮层图像中的神经元及其空间坐标是定量分析大脑空间组织的必要步骤。这在阿尔茨海默病(AD)的研究中尤其重要，在AD中，空间神经元的组织和关系因神经元丢失而高度中断。为了利用人脑组织的高分辨率共聚焦显微镜图像实现神经元识别的自动化，我们提出了一种基于统计物理的识别方法，该方法包括图像预处理、并行图像分割和基于形状、光密度和大小的聚类选择。通过对AQ态非均匀Potts模型的蒙特卡罗模拟，我们将经过预处理的数字图像分割成簇。然后，我们选择POTS分割参数的范围以产生对测试图像中简化对象的理想识别。我们将我们的并行分割方法应用于对照组和AD患者，获得了98%(对于对照组)和93%(对于AD患者)的识别率，最多有3%的错误聚类。
Identifying neurons and their spatial coordinates in images of the cerebral cortex is a necessary step in the quantitative analysis of spatial organization in the brain. This is especially important in the study of Alzheimer's disease (AD), in which spatial neuronal organization and relationships are highly disrupted because of neuronal loss. To automate neuron recognition by using high-resolution confocal microscope images from human brain tissue, we propose a recognition method based on statistical physics that consists of image preprocessing, parallel image segmentation, and cluster selection on the basis of shape, optical density, and size. We segment a preprocessed digital image into clusters by applying Monte Carlo simulations of aq-state inhomogeneous Potts model. We then select the range of Potts segmentation parameters to yield an ideal recognition of simplified objects in the test image. We apply our parallel segmentation method to control individuals and to AD patients and achieve recognition of 98% (for a control) and 93% (for an AD patient), with at most 3% false clusters.