Automatic thresholding of three-dimensional microvascular structures from confocal microscopy images.
Automatic thresholding of three-dimensional microvascular structures from confocal microscopy images.
复制标题
从共焦显微镜图像中自动阈值化三维微血管结构。
DOI：
10.1111/j.1365-2818.2007.01739.x
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发表时间：
2007
影响因子：
2
通讯作者：
Hoying,JamesB
中科院分区：
文献类型：
--
作者：
Smith,CynthiaM;ColeSmith,J;Williams,StuartK;Rodriguez,JeffreyJ;Hoying,JamesB
我们结合了共焦显微镜、图像处理和优化技术，以获得微脉管系统的自动、准确的体积测量。最初，我们制作了含有悬浮在 I 型胶原中的 15 µm FocalCheck™ 微球的组织模型。使用这些模型，我们获得了一堆共焦图像，并检查了各种阈值方案的准确性。文献中使用单峰直方图、双峰直方图或基于强度和边缘的算法的阈值算法都显着高估了图像堆栈中前景结构的体积。相反，我们开发了一种启发式技术，根据每个体素的深度、强度和（可选）梯度自动确定高质量阈值。该方法分析了每个单独图像堆栈的强度和梯度阈值方法，并考虑了堆栈更深图像中看到的强度衰减。最后，我们生成了一个由嵌入 I 型胶原蛋白凝胶中的大鼠脂肪微血管碎片组成的微血管结构，并获得了一堆共焦图像。使用我们新的阈值方案，我们能够获得生长的微血管碎片的自动体积测量。
We have combined confocal microscopy, image processing, and optimization techniques to obtain automated, accurate volumetric measurements of microvasculature. Initially, we made tissue phantoms containing 15‐μm FocalCheck™ microspheres suspended in type I collagen. Using these phantoms we obtained a stack of confocal images and examined the accuracy of various thresholding schemes. Thresholding algorithms from the literature that utilize a unimodal histogram, a bimodal histogram, or an intensity and edge‐based algorithm all significantly overestimated the volume of foreground structures in the image stack. Instead, we developed a heuristic technique to automatically determine good‐quality threshold values based on the depth, intensity, and (optionally) gradient of each voxel. This method analyzed intensity and gradient threshold methods for each individual image stack, taking into account the intensity attenuation that is seen in deeper images of the stack. Finally, we generated a microvascular construct comprised of rat fat microvessel fragments embedded in collagen I gels and obtained stacks of confocal images. Using our new thresholding scheme we were able to obtain automatic volume measurements of growing microvessel fragments.