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Alternative Parametric Boundary Reconstruction Method for Biomedical Imaging
Determining the outline or boundary contour of a two-dimensional object, or the surface of a three-dimensional object poses difficulties particularly when there is substantial measurement noise or uncertainty. By adapting the mathematical approach of stochastic function recovery to this task, it is...
Autores principales: | , |
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Formato: | Texto |
Lenguaje: | English |
Publicado: |
Hindawi Publishing Corporation
2008
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2366076/ https://www.ncbi.nlm.nih.gov/pubmed/18464920 http://dx.doi.org/10.1155/2008/623475 |
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author | Kolibal, Joseph Howard, Daniel |
author_facet | Kolibal, Joseph Howard, Daniel |
author_sort | Kolibal, Joseph |
collection | PubMed |
description | Determining the outline or boundary contour of a two-dimensional object, or the surface of a three-dimensional object poses difficulties particularly when there is substantial measurement noise or uncertainty. By adapting the mathematical approach of stochastic function recovery to this task, it is possible to obtain usable estimates for these boundaries, even in the presence of large amounts of noise. The technique is applied to parametric boundary data and has potential applications in biomedical imaging. It should be considered as one of several techniques to improve the visualization of images. |
format | Text |
id | pubmed-2366076 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | Hindawi Publishing Corporation |
record_format | MEDLINE/PubMed |
spelling | pubmed-23660762008-05-08 Alternative Parametric Boundary Reconstruction Method for Biomedical Imaging Kolibal, Joseph Howard, Daniel J Biomed Biotechnol Research Article Determining the outline or boundary contour of a two-dimensional object, or the surface of a three-dimensional object poses difficulties particularly when there is substantial measurement noise or uncertainty. By adapting the mathematical approach of stochastic function recovery to this task, it is possible to obtain usable estimates for these boundaries, even in the presence of large amounts of noise. The technique is applied to parametric boundary data and has potential applications in biomedical imaging. It should be considered as one of several techniques to improve the visualization of images. Hindawi Publishing Corporation 2008 2008-04-21 /pmc/articles/PMC2366076/ /pubmed/18464920 http://dx.doi.org/10.1155/2008/623475 Text en Copyright © 2008 J. Kolibal and D. Howard. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Kolibal, Joseph Howard, Daniel Alternative Parametric Boundary Reconstruction Method for Biomedical Imaging |
title | Alternative Parametric Boundary Reconstruction Method for Biomedical Imaging |
title_full | Alternative Parametric Boundary Reconstruction Method for Biomedical Imaging |
title_fullStr | Alternative Parametric Boundary Reconstruction Method for Biomedical Imaging |
title_full_unstemmed | Alternative Parametric Boundary Reconstruction Method for Biomedical Imaging |
title_short | Alternative Parametric Boundary Reconstruction Method for Biomedical Imaging |
title_sort | alternative parametric boundary reconstruction method for biomedical imaging |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2366076/ https://www.ncbi.nlm.nih.gov/pubmed/18464920 http://dx.doi.org/10.1155/2008/623475 |
work_keys_str_mv | AT kolibaljoseph alternativeparametricboundaryreconstructionmethodforbiomedicalimaging AT howarddaniel alternativeparametricboundaryreconstructionmethodforbiomedicalimaging |