Unal Gozde, Bucher Susann, Carlier Stephane, Slabaugh Greg, Fang Tong, Tanaka Kaoru
Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey.
IEEE Trans Inf Technol Biomed. 2008 May;12(3):335-47. doi: 10.1109/titb.2008.920620.
Segmentation of arterial wall boundaries from intravascular images is an important problem for many applications in the study of plaque characteristics, mechanical properties of the arterial wall, its 3-D reconstruction, and its measurements such as lumen size, lumen radius, and wall radius. We present a shape-driven approach to segmentation of the arterial wall from intravascular ultrasound images in the rectangular domain. In a properly built shape space using training data, we constrain the lumen and media-adventitia contours to a smooth, closed geometry, which increases the segmentation quality without any tradeoff with a regularizer term. In addition to a shape prior, we utilize an intensity prior through a nonparametric probability-density-based image energy, with global image measurements rather than pointwise measurements used in previous methods. Furthermore, a detection step is included to address the challenges introduced to the segmentation process by side branches and calcifications. All these features greatly enhance our segmentation method. The tests of our algorithm on a large dataset demonstrate the effectiveness of our approach.
从血管内图像中分割动脉壁边界对于斑块特征研究、动脉壁力学特性、三维重建以及诸如管腔大小、管腔半径和壁半径等测量的许多应用来说是一个重要问题。我们提出了一种形状驱动的方法，用于在矩形域内从血管内超声图像中分割动脉壁。在使用训练数据构建的合适形状空间中，我们将管腔和中膜 - 外膜轮廓约束为光滑的闭合几何形状，这在不与正则化项进行任何权衡的情况下提高了分割质量。除了形状先验，我们还通过基于非参数概率密度的图像能量利用强度先验，使用全局图像测量而非先前方法中使用的逐点测量。此外，还包括一个检测步骤来解决侧支和钙化给分割过程带来的挑战。所有这些特性极大地增强了我们的分割方法。我们的算法在大型数据集上的测试证明了我们方法的有效性。
