Segmentation for MRA image: An improved level-set approach
Segmentation for MRA image: An improved level-set approach
复制标题
MRA 图像分割：改进的水平集方法
DOI：
10.1109/tim.2007.899839
复制
发表时间：
2007-08-01
影响因子：
5.6
通讯作者：
Wang, Qiang
中科院分区：
文献类型：
--
作者：
Hao, Jiasheng;Shen, Yi;Wang, Qiang
体积数据的无监督分割仍然是一项具有挑战性的任务。近年来，水平集方法受到了广泛的关注，它结合了全局平滑性和拓扑变化的灵活性，并且比传统的统计分类具有显着的优势。然而，水平集方法由于大量迭代而承受着沉重的计算负担。我们提出了一种基于分水岭算法的快速水平集框架，用于从体积数据集中分割复杂结构。驱动应用是从磁共振血管造影中分割 3D 人体脑血管结构，由于血管几何形状和强度模式的复杂性，这是一个非常具有挑战性的分割问题。实验结果表明，该方法能够实现快速、准确的良好分割。
Unsupervised segmentation of volumetric data is still a challenging task. Recently, level-set methods have received a great deal of attention, which combine global smoothness with the flexibility of topology changes and offer significant advantages over conventional statistical classification. However, level-set methods suffer from heavy computational burden because of a lot of iterations. We present a fast level-set framework based on the watershed algorithm for the segmentation of complicated structures from a volumetric data set. The driving application is the segmentation of 3-D human cerebrovascular structures from magnetic resonance angiography, which is known to be a very challenging segmentation problem due to the complexity of vessel geometry and intensity patterns. Experimental results show that the proposed method gives fast and accurate excellent segmentation.