Techniques to derive geometries for image-based Eulerian computations.
Techniques to derive geometries for image-based Eulerian computations.
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DOI：
10.1108/ec-06-2012-0145
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发表时间：
2014
影响因子：
1.6
通讯作者：
Udaykumar HS
中科院分区：
文献类型：
--
作者：
Dillard S;Buchholz J;Vigmostad S;Kim H;Udaykumar HS
三个常用的水平集为基础的分割方法的性能进行检查的目的，定义功能和边界条件，基于图像的欧拉流体和固体力学模型。评估的重点是确定一种方法，从计算流体/固体建模的角度来看，产生最佳的几何表示。特别是，从各种各样的成像方式和噪声强度的几何形状的提取，以提供一个沉浸的边界的方法，是有针对性的。二维和三维图像，从光学，X射线CT，和超声成像模式，采集的分割与活动轮廓，k-均值，和自适应聚类方法。分割轮廓将转换为水平集，并根据需要进行平滑处理，以便在流体/固体模拟中使用。这三种方法产生的结果进行了比较，视觉和对比度，信噪比和对比度噪声比的措施。虽然活动轮廓方法具有内置的平滑和正则化，并产生连续的轮廓，聚类方法（k-means和自适应聚类）产生离散（像素化）的轮廓，需要使用斑点减少各向异性扩散（SRAD）平滑。因此，对于具有高对比度和低到中等噪声的图像，活动轮廓通常是优选的。然而，自适应聚类被认为是远远上级其他两种方法的图像具有高水平的噪声和全球强度变化，由于其更复杂的使用本地像素/体素强度统计。通常很难先验地知道对于给定的图像类型哪种分割将执行得最好，特别是当几何建模是最终目标时。这项工作提供了洞察力的算法选择过程中，以及概述了一个实用的框架生成有用的几何曲面的欧拉设置。
The performance of three frequently used level set-based segmentation methods is examined for the purpose of defining features and boundary conditions for image-based Eulerian fluid and solid mechanics models. The focus of the evaluation is to identify an approach that produces the best geometric representation from a computational fluid/solid modeling point of view. In particular, extraction of geometries from a wide variety of imaging modalities and noise intensities, to supply to an immersed boundary approach, is targeted. Two- and three-dimensional images, acquired from optical, X-ray CT, and ultrasound imaging modalities, are segmented with active contours, k-means, and adaptive clustering methods. Segmentation contours are converted to level sets and smoothed as necessary for use in fluid/solid simulations. Results produced by the three approaches are compared visually and with contrast ratio, signal-to-noise ratio, and contrast-to-noise ratio measures. While the active contours method possesses built-in smoothing and regularization and produces continuous contours, the clustering methods (k-means and adaptive clustering) produce discrete (pixelated) contours that require smoothing using speckle-reducing anisotropic diffusion (SRAD). Thus, for images with high contrast and low to moderate noise, active contours are generally preferable. However, adaptive clustering is found to be far superior to the other two methods for images possessing high levels of noise and global intensity variations, due to its more sophisticated use of local pixel/voxel intensity statistics. It is often difficult to know a priori which segmentation will perform best for a given image type, particularly when geometric modeling is the ultimate goal. This work offers insight to the algorithm selection process, as well as outlining a practical framework for generating useful geometric surfaces in an Eulerian setting.