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Phase contrast cell detection using multilevel classification

Ehab Essa, Xianghua Xie Orcid Logo

International Journal for Numerical Methods in Biomedical Engineering, Start page: e2916

Swansea University Author: Xianghua Xie Orcid Logo

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DOI (Published version): 10.1002/cnm.2916

Abstract

In this paper, we propose a fully automated learning based approach for detecting cells in time-lapse phase contrast images. The proposed system combines two machine learning approaches to achieve bottom-up image segmentation.

Published in: International Journal for Numerical Methods in Biomedical Engineering
ISSN: 20407939
Published: 2017
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URI: https://cronfa.swan.ac.uk/Record/cronfa34727
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first_indexed 2017-07-22T20:25:36Z
last_indexed 2018-02-09T05:25:08Z
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spelling 2018-01-30T11:10:55.0038346 v2 34727 2017-07-22 Phase contrast cell detection using multilevel classification b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2017-07-22 SCS In this paper, we propose a fully automated learning based approach for detecting cells in time-lapse phase contrast images. The proposed system combines two machine learning approaches to achieve bottom-up image segmentation. Journal Article International Journal for Numerical Methods in Biomedical Engineering e2916 20407939 23 8 2017 2017-08-23 10.1002/cnm.2916 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2018-01-30T11:10:55.0038346 2017-07-22T16:52:31.4691472 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Ehab Essa 1 Xianghua Xie 0000-0002-2701-8660 2 0034727-22072017165322.pdf eexx_IJNMBE_v6.pdf 2017-07-22T16:53:22.8100000 Output 14170357 application/pdf Accepted Manuscript true 2018-07-28T00:00:00.0000000 true eng
title Phase contrast cell detection using multilevel classification
spellingShingle Phase contrast cell detection using multilevel classification
Xianghua Xie
title_short Phase contrast cell detection using multilevel classification
title_full Phase contrast cell detection using multilevel classification
title_fullStr Phase contrast cell detection using multilevel classification
title_full_unstemmed Phase contrast cell detection using multilevel classification
title_sort Phase contrast cell detection using multilevel classification
author_id_str_mv b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author Xianghua Xie
author2 Ehab Essa
Xianghua Xie
format Journal article
container_title International Journal for Numerical Methods in Biomedical Engineering
container_start_page e2916
publishDate 2017
institution Swansea University
issn 20407939
doi_str_mv 10.1002/cnm.2916
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description In this paper, we propose a fully automated learning based approach for detecting cells in time-lapse phase contrast images. The proposed system combines two machine learning approaches to achieve bottom-up image segmentation.
published_date 2017-08-23T03:43:05Z
_version_ 1763752002963111936
score 11.036706

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