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An efficient, scalable, and adaptable framework for solving generic systems of level-set PDEs
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 term...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3872740/ https://www.ncbi.nlm.nih.gov/pubmed/24501592 http://dx.doi.org/10.3389/fninf.2013.00035 |
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author | Mosaliganti, Kishore R. Gelas, Arnaud Megason, Sean G. |
author_facet | Mosaliganti, Kishore R. Gelas, Arnaud Megason, Sean G. |
author_sort | Mosaliganti, Kishore R. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-3872740 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-38727402014-02-05 An efficient, scalable, and adaptable framework for solving generic systems of level-set PDEs Mosaliganti, Kishore R. Gelas, Arnaud Megason, Sean G. Front Neuroinform Neuroscience 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. Frontiers Media S.A. 2013-12-26 /pmc/articles/PMC3872740/ /pubmed/24501592 http://dx.doi.org/10.3389/fninf.2013.00035 Text en Copyright © 2013 Mosaliganti, Gelas and Megason. http://creativecommons.org/licenses/by/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Mosaliganti, Kishore R. Gelas, Arnaud Megason, Sean G. An efficient, scalable, and adaptable framework for solving generic systems of level-set PDEs |
title | An efficient, scalable, and adaptable framework for solving generic systems of level-set PDEs |
title_full | An efficient, scalable, and adaptable framework for solving generic systems of level-set PDEs |
title_fullStr | An efficient, scalable, and adaptable framework for solving generic systems of level-set PDEs |
title_full_unstemmed | An efficient, scalable, and adaptable framework for solving generic systems of level-set PDEs |
title_short | An efficient, scalable, and adaptable framework for solving generic systems of level-set PDEs |
title_sort | efficient, scalable, and adaptable framework for solving generic systems of level-set pdes |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3872740/ https://www.ncbi.nlm.nih.gov/pubmed/24501592 http://dx.doi.org/10.3389/fninf.2013.00035 |
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