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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...

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Detalles Bibliográficos
Autores principales: Ungru, Kathrin, Jiang, Xiaoyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Research Network of Computational and Structural Biotechnology 2017
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.
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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
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