Gooya Ali, Liao Hongen, Matsumiya Kiyoshi, Masamune Ken, Masutani Yoshitaka, Dohi Takeyoshi
Graduate School of Engineering, The University of Tokyo, Tokyo, Japan.
IEEE Trans Image Process. 2008 Aug;17(8):1295-312. doi: 10.1109/TIP.2008.925378.
In this paper, a level-set-based geometric regularization method is proposed which has the ability to estimate the local orientation of the evolving front and utilize it as shape induced information for anisotropic propagation. We show that preserving anisotropic fronts can improve elongations of the extracted structures, while minimizing the risk of leakage. To that end, for an evolving front using its shape-offset level-set representation, a novel energy functional is defined. It is shown that constrained optimization of this functional results in an anisotropic expansion flow which is usefull for vessel segmentation. We have validated our method using synthetic data sets, 2-D retinal angiogram images and magnetic resonance angiography volumetric data sets. A comparison has been made with two state-of-the-art vessel segmentation methods. Quantitative results, as well as qualitative comparisons of segmentations, indicate that our regularization method is a promising tool to improve the efficiency of both techniques.
本文提出了一种基于水平集的几何正则化方法，该方法能够估计演化前沿的局部方向，并将其用作各向异性传播的形状诱导信息。我们表明，保留各向异性前沿可以改善提取结构的伸长率，同时将泄漏风险降至最低。为此，对于使用其形状偏移水平集表示的演化前沿，定义了一种新颖的能量泛函。结果表明，对该泛函进行约束优化会产生一种各向异性膨胀流，这对于血管分割很有用。我们使用合成数据集、二维视网膜血管造影图像和磁共振血管造影体积数据集对我们的方法进行了验证。已与两种最先进的血管分割方法进行了比较。定量结果以及分割的定性比较表明，我们的正则化方法是提高这两种技术效率的一种很有前途的工具。
