Tang Jinshan, Acton Scott T
Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904-4743, USA.
IEEE Trans Biomed Eng. 2004 Feb;51(2):316-24. doi: 10.1109/TBME.2003.820374.
Due to movement of the specimen, vasodilation, and intense clutter, the intravital location of a vessel boundary from video microscopy is a difficult but necessary task in analyzing the mechanics of inflammation and the structure of the microvasculature. This paper details an active contour model for vessel boundary detection and tracking. In developing the method, two innovations are introduced. First, the B-spline model is combined with the gradient vector flow (GVF) external force. Second, a multiscale gradient vector flow (MSGVF) is employed to elude clutter and to reliably localize the vessel boundaries. Using synthetic experiments and video microscopy obtained via transillumination of the mouse cremaster muscle, we demonstrate that the MSGVF approach is superior to the fixed-scale GVF approach in terms of boundary localization. In each experiment, the fixed scale approach yielded at least a 50% increase in root mean squared error over the multiscale approach. In addition to delineating the vessel boundary so that cells can be detected and tracked, we demonstrate the boundary location technique enables automatic blood flow velocity computation in vivo.
由于样本的移动、血管舒张以及严重的杂波干扰，通过视频显微镜确定活体血管边界的位置在分析炎症机制和微血管结构时是一项艰巨但必要的任务。本文详细介绍了一种用于血管边界检测和跟踪的主动轮廓模型。在开发该方法时，引入了两项创新。第一，将B样条模型与梯度向量流（GVF）外力相结合。第二，采用多尺度梯度向量流（MSGVF）来避开杂波并可靠地定位血管边界。通过合成实验以及对小鼠提睾肌进行透照获得的视频显微镜图像，我们证明在边界定位方面，MSGVF方法优于固定尺度的GVF方法。在每个实验中，固定尺度方法的均方根误差比多尺度方法至少高出50%。除了描绘血管边界以便能够检测和跟踪细胞外，我们还证明了这种边界定位技术能够在体内自动计算血流速度。
