体积数据的自动分割与形状特征描述
Automated segmentation and shape characterization of volumetric data.
作者信息
Galinsky Vitaly L, Frank Lawrence R
机构信息
Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, CA 92093-0854, USA; Electrical and Computer Engineering Department, University of California at San Diego, La Jolla, CA 92093-0407, USA.
Center for Scientific Computation in Imaging, University of California at San Diego, La Jolla, CA 92093-0854, USA; Center for Functional MRI, University of California at San Diego, La Jolla, CA 92093-0677, USA.
出版信息
Neuroimage. 2014 May 15;92:156-68. doi: 10.1016/j.neuroimage.2014.01.053. Epub 2014 Feb 9.
Characterization of complex shapes embedded within volumetric data is an important step in a wide range of applications. Standard approaches to this problem employ surface-based methods that require inefficient, time consuming, and error prone steps of surface segmentation and inflation to satisfy the uniqueness or stability of subsequent surface fitting algorithms. Here we present a novel method based on a spherical wave decomposition (SWD) of the data that overcomes several of these limitations by directly analyzing the entire data volume, obviating the segmentation, inflation, and surface fitting steps, significantly reducing the computational time and eliminating topological errors while providing a more detailed quantitative description based upon a more complete theoretical framework of volumetric data. The method is demonstrated and compared to the current state-of-the-art neuroimaging methods for segmentation and characterization of volumetric magnetic resonance imaging data of the human brain.
对体数据中嵌入的复杂形状进行特征化是广泛应用中的重要一步。解决这个问题的标准方法采用基于表面的方法，这些方法需要进行效率低下、耗时且容易出错的表面分割和膨胀步骤，以满足后续表面拟合算法的唯一性或稳定性。在此，我们提出一种基于数据球面波分解（SWD）的新方法，该方法通过直接分析整个数据体克服了其中一些限制，避免了分割、膨胀和表面拟合步骤，显著减少了计算时间并消除了拓扑错误，同时基于更完整的体数据理论框架提供了更详细的定量描述。该方法通过对用于人类大脑体磁共振成像数据分割和特征化的当前最先进神经成像方法进行了演示和比较。
Zotero 插件