An efficient, scalable, and adaptable framework for solving generic systems of level-set PDEs

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An efficient, scalable, and adaptable framework for solving generic systems of level-set PDEs

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Title: An efficient, scalable, and adaptable framework for solving generic systems of level-set PDEs
Author: Mosaliganti, Kishore R.; Gelas, Arnaud; Megason, Sean G.

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Citation: Mosaliganti, Kishore R., Arnaud Gelas, and Sean G. Megason. 2013. “An efficient, scalable, and adaptable framework for solving generic systems of level-set PDEs.” Frontiers in Neuroinformatics 7 (1): 35. doi:10.3389/fninf.2013.00035. http://dx.doi.org/10.3389/fninf.2013.00035.
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Abstract: 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.
Published Version: doi:10.3389/fninf.2013.00035
Other Sources: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3872740/pdf/
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:11879903
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