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Dynamic Programming Based Segmentation in Biomedical Imaging
Many applications in biomedical imaging have a demand on automatic detection of lines, contours, or boundaries of bones, organs, vessels, and cells. Aim is to support expert decisions in interactive applications or to include it as part of a processing pipeline for automatic image analysis. Biomedic...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Research Network of Computational and Structural Biotechnology
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5338725/ https://www.ncbi.nlm.nih.gov/pubmed/28289536 http://dx.doi.org/10.1016/j.csbj.2017.02.001 |
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author | Ungru, Kathrin Jiang, Xiaoyi |
author_facet | Ungru, Kathrin Jiang, Xiaoyi |
author_sort | Ungru, Kathrin |
collection | PubMed |
description | Many applications in biomedical imaging have a demand on automatic detection of lines, contours, or boundaries of bones, organs, vessels, and cells. Aim is to support expert decisions in interactive applications or to include it as part of a processing pipeline for automatic image analysis. Biomedical images often suffer from noisy data and fuzzy edges. Therefore, there is a need for robust methods for contour and line detection. Dynamic programming is a popular technique that satisfies these requirements in many ways. This work gives a brief overview over approaches and applications that utilize dynamic programming to solve problems in the challenging field of biomedical imaging. |
format | Online Article Text |
id | pubmed-5338725 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Research Network of Computational and Structural Biotechnology |
record_format | MEDLINE/PubMed |
spelling | pubmed-53387252017-03-13 Dynamic Programming Based Segmentation in Biomedical Imaging Ungru, Kathrin Jiang, Xiaoyi Comput Struct Biotechnol J Short Survey Many applications in biomedical imaging have a demand on automatic detection of lines, contours, or boundaries of bones, organs, vessels, and cells. Aim is to support expert decisions in interactive applications or to include it as part of a processing pipeline for automatic image analysis. Biomedical images often suffer from noisy data and fuzzy edges. Therefore, there is a need for robust methods for contour and line detection. Dynamic programming is a popular technique that satisfies these requirements in many ways. This work gives a brief overview over approaches and applications that utilize dynamic programming to solve problems in the challenging field of biomedical imaging. Research Network of Computational and Structural Biotechnology 2017-02-16 /pmc/articles/PMC5338725/ /pubmed/28289536 http://dx.doi.org/10.1016/j.csbj.2017.02.001 Text en © 2017 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Short Survey Ungru, Kathrin Jiang, Xiaoyi Dynamic Programming Based Segmentation in Biomedical Imaging |
title | Dynamic Programming Based Segmentation in Biomedical Imaging |
title_full | Dynamic Programming Based Segmentation in Biomedical Imaging |
title_fullStr | Dynamic Programming Based Segmentation in Biomedical Imaging |
title_full_unstemmed | Dynamic Programming Based Segmentation in Biomedical Imaging |
title_short | Dynamic Programming Based Segmentation in Biomedical Imaging |
title_sort | dynamic programming based segmentation in biomedical imaging |
topic | Short Survey |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5338725/ https://www.ncbi.nlm.nih.gov/pubmed/28289536 http://dx.doi.org/10.1016/j.csbj.2017.02.001 |
work_keys_str_mv | AT ungrukathrin dynamicprogrammingbasedsegmentationinbiomedicalimaging AT jiangxiaoyi dynamicprogrammingbasedsegmentationinbiomedicalimaging |