Santamaría-Pang A, Colbert C M, Saggau P, Kakadiaris I A
Computational Biomedicine Lab, Dept. of CS, Univ. of Houston, Houston, TX, USA.
Med Image Comput Comput Assist Interv. 2007;10(Pt 2):486-94. doi: 10.1007/978-3-540-75759-7_59.
In this paper, we present a general framework for extracting 3D centerlines from volumetric datasets. Unlike the majority of previous approaches, we do not require a prior segmentation of the volume nor we do assume any particular tubular shape. Centerline extraction is performed using a morphology-guided level set model. Our approach consists of: i) learning the structural patterns of a tubular-like object, and ii) estimating the centerline of a tubular object as the path with minimal cost with respect to outward flux in gray level images. Such shortest path is found by solving the Eikonal equation. We compare the performance of our method with existing approaches in synthetic, CT, and multiphoton 3D images, obtaining substantial improvements, especially in the case of irregular tubular objects.
在本文中，我们提出了一个从体数据集中提取三维中心线的通用框架。与大多数先前的方法不同，我们不需要对体积进行预先分割，也不假设任何特定的管状形状。中心线提取是使用形态学引导的水平集模型进行的。我们的方法包括：i）学习管状物体的结构模式，以及ii）将管状物体的中心线估计为灰度图像中相对于向外通量具有最小成本的路径。通过求解程函方程找到这样的最短路径。我们将我们的方法与合成图像、CT图像和多光子三维图像中的现有方法的性能进行了比较，取得了显著的改进，特别是在不规则管状物体的情况下。
