Volumetric segmentation using Weibull E-SD fields
Volumetric segmentation using Weibull E-SD fields
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DOI：
10.1109/tvcg.2003.1207440
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发表时间：
2003-07-01
影响因子：
5.2
通讯作者：
Capco, DG
中科院分区：
文献类型：
--
作者：
Hu, JX;Razdan, A;Capco, DG
提出了一种对体数据中出现灰度的目标进行粗粒度分割的方法。输入数据位于顶点v(i，j，k)的3D结构化网格上，每个顶点与标量值相关联。本文将体素看作kappa×kappa×kappa，立方体，每个体素被赋予两个值：期望和标准差(E-SD)。我们使用威布尔噪声指数来估计体素中的噪声，并获得每个体素更精确的E-SD值。我们绘制了具有相同E-SD的体素的频率，然后提出了基于威布尔E-SD场的三维分割。我们的试验台包括来自共聚焦激光扫描显微镜(CLSM)的合成数据和实际体积数据。对这些数据的分析都显示了其E-SD领域的明显和明确的区域。在E-SD场的指导下，我们可以有效地分割嵌入到真实和模拟3D数据中的对象。
This paper presents a coarse-grain approach for segmentation of objects with gray levels appearing in volume data. The input data is on a 3D structured grid of vertices v(i, j, k), each associated with a scalar value. In this paper, we consider a voxel as a kappa x kappa x kappa, cube and each voxel is assigned two values: expectancy and standard deviation (E-SD). We use the Weibull noise index to estimate the noise in a voxel and to obtain more precise E-SD values for each voxel. We plot the frequency of voxels which have the same E-SD, then 3D segmentation based on the Weibull E-SD field is presented. Our test bed includes synthetic data as well as real volume data from a confocal laser scanning microscope (CLSM). Analysis of these data all show distinct and defining regions in their E-SD fields. Under the guide of the E-SD field, we can efficiently segment the objects embedded in real and simulated 3D data.