Xu Lei, Stojkovic Branislav, Zhu Yongding, Song Qi, Wu Xiaodong, Sonka Milan, Xu Jinhui
Department of Computer Science and Engineering State University of New York at Buffalo, Buffalo, NY 14260, USA.
Inf Process Med Imaging. 2011;22:208-20. doi: 10.1007/978-3-642-22092-0_18.
Despite extensive studies in the past, the problem of segmenting globally optimal single and multiple surfaces in 3D volumetric images remains challenging in medical imaging. The problem becomes even harder in highly noisy and edge-weak images. In this paper we present a novel and highly efficient graph-theoretical iterative method with bi-criteria of global optimality and smoothness for both single and multiple surfaces. Our approach is based on a volumetric graph representation of the 3D image that incorporates curvature information. To evaluate the convergence and performance of our method, we test it on a set of 14 3D OCT images. Our experiments suggest that the proposed method yields optimal (or almost optimal) solutions in 3 to 5 iterations. To the best of our knowledge, this is the first algorithm that utilizes curvature in objective function to ensure the smoothness of the generated surfaces while striving for achieving global optimality. Comparing to the best existing approaches, our method has a much improved running time, yields almost the same global optimality but with much better smoothness, which makes it especially suitable for segmenting highly noisy images.
尽管过去进行了广泛的研究，但在医学成像中，对三维体积图像中的全局最优单表面和多表面进行分割的问题仍然具有挑战性。在高噪声和边缘模糊的图像中，这个问题变得更加困难。在本文中，我们提出了一种新颖且高效的基于图论的迭代方法，该方法具有全局最优性和光滑性的双准则，适用于单表面和多表面。我们的方法基于包含曲率信息的三维图像的体积图表示。为了评估我们方法的收敛性和性能，我们在一组14幅三维光学相干断层扫描（OCT）图像上对其进行了测试。我们的实验表明，所提出的方法在3到5次迭代中产生最优（或几乎最优）解。据我们所知，这是第一种在目标函数中利用曲率来确保生成表面的光滑性同时力求实现全局最优性的算法。与现有的最佳方法相比，我们的方法运行时间有了很大改进，产生的全局最优性几乎相同，但光滑性更好，这使得它特别适合于分割高噪声图像。
