An efficient, scalable, and adaptable framework for solving generic systems of level-set PDEs.
An efficient, scalable, and adaptable framework for solving generic systems of level-set PDEs.
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DOI：
10.3389/fninf.2013.00035
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发表时间：
2013
影响因子：
3.5
通讯作者：
Megason SG
中科院分区：
文献类型：
--
作者：
Mosaliganti KR;Gelas A;Megason SG
在过去的十年中，水平集方法在图像配准、分割、跟踪和重建中的应用得到了积极的发展。然而，各种各样的水平集偏微分方程组及其数值离散化方案的发展，加上偏微分方程组项、停止准则和重新初始化策略的混合组合，已经产生了软件物流问题。在缺乏综合设计的情况下，目前的工具包只支持特定类型的水平集实现，这限制了未来的算法开发，因为扩展需要大量的代码重复和工作。在新的NIH/NLM洞察工具包(ITK)v4架构中，我们实现了一个水平集软件设计，它可以灵活地处理不同的数值(连续、离散和稀疏)和网格表示(点、网格和基于图像)。鉴于通用PDE是不同术语的总和，我们使用了一组链接的容器，可以在演化过程中的任何点向其添加或删除水平集术语。这种基于容器的方法允许用户在编译时以灵活的方式探索和定制Level-Set公式中的项。该框架被优化，从而消除了跨多个项的公共强度函数(例如，梯度和海森)的重复计算。该框架还支持用于多对象分割和处理大数据集的多水平集的演化。为此，我们将水平集域限制为图像域的子集，并使用多线程策略来处理子域或水平集函数组。用户还可以从各种重新初始化策略和停止条件中进行选择。最后，我们开发了一个可视化框架，实时显示水平集的演化，以帮助指导算法开发和参数优化。我们使用斑马鱼胚胎发育中的细胞的共聚焦显微镜图像来展示我们的新框架的力量。
In the last decade, level-set methods have been actively developed for applications in image registration, segmentation, tracking, and reconstruction. However, the development of a wide variety of level-set PDEs and their numerical discretization schemes, coupled with hybrid combinations of PDE terms, stopping criteria, and reinitialization strategies, has created a software logistics problem. In the absence of an integrative design, current toolkits support only specific types of level-set implementations which restrict future algorithm development since extensions require significant code duplication and effort. In the new NIH/NLM Insight Toolkit (ITK) v4 architecture, we implemented a level-set software design that is flexible to different numerical (continuous, discrete, and sparse) and grid representations (point, mesh, and image-based). Given that a generic PDE is a summation of different terms, we used a set of linked containers to which level-set terms can be added or deleted at any point in the evolution process. This container-based approach allows the user to explore and customize terms in the level-set equation at compile-time in a flexible manner. The framework is optimized so that repeated computations of common intensity functions (e.g., gradient and Hessians) across multiple terms is eliminated. The framework further enables the evolution of multiple level-sets for multi-object segmentation and processing of large datasets. For doing so, we restrict level-set domains to subsets of the image domain and use multithreading strategies to process groups of subdomains or level-set functions. Users can also select from a variety of reinitialization policies and stopping criteria. Finally, we developed a visualization framework that shows the evolution of a level-set in real-time to help guide algorithm development and parameter optimization. We demonstrate the power of our new framework using confocal microscopy images of cells in a developing zebrafish embryo.
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影响因子：
10.6
作者：
Chan, TF;Vese, LA
通讯作者：
Vese, LA
DOI：
10.1109/34.368173
发表时间：
1995-02-01
影响因子：
23.6
作者：
MALLADI, R;SETHIAN, JA;VEMURI, BC
通讯作者：
VEMURI, BC
影响因子：
10.6
作者：
Dzyubachyk, Oleh;van Cappellen, Wiggert A.;Meijering, Erik
通讯作者：
Meijering, Erik
影响因子：
4
作者：
RUDIN, LI;OSHER, S;FATEMI, E
通讯作者：
FATEMI, E
影响因子：
19.5
作者：
KASS, M;WITKIN, A;TERZOPOULOS, D
通讯作者：
TERZOPOULOS, D