Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images
Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorith...
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| description | Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells. |
| doi_str_mv | 10.1371/journal.pone.0130178 |
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As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0130178</identifier><identifier>PMID: 26066315</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Algorithms ; Biological effects ; Boundaries ; Cell Count ; Cell Line ; Cell Shape - physiology ; Cell Size ; Field of view ; Humans ; Image contrast ; Image detection ; Image processing ; Image segmentation ; International conferences ; Light ; Light intensity ; Luminous intensity ; Mechanical engineering ; Medical imaging ; Methods ; Microscopy ; Microscopy, Phase-Contrast - methods ; Morphology ; Phase contrast ; Reproducibility of Results ; Robotics ; Segmentation ; Therapeutic applications ; Time-Lapse Imaging - methods</subject><ispartof>PloS one, 2015-06, Vol.10 (6), p.e0130178-e0130178</ispartof><rights>2015 Wang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2015 Wang et al 2015 Wang et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c526t-74f45dc8b9d18503192df990a511a9e60097da43edae0f4109f426c23db703113</citedby><cites>FETCH-LOGICAL-c526t-74f45dc8b9d18503192df990a511a9e60097da43edae0f4109f426c23db703113</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4467081/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4467081/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79343,79344</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/26066315$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Wang, Yuliang</creatorcontrib><creatorcontrib>Zhang, Zaicheng</creatorcontrib><creatorcontrib>Wang, Huimin</creatorcontrib><creatorcontrib>Bi, Shusheng</creatorcontrib><title>Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. At last, the proposed algorithm is applied to the negative phase contrast images from different experiments. The performance of the proposed method is evaluated. Results show that the proposed method can achieve optimized cell boundary detection and highly accurate segmentation for clustered cells.</description><subject>Algorithms</subject><subject>Biological effects</subject><subject>Boundaries</subject><subject>Cell Count</subject><subject>Cell Line</subject><subject>Cell Shape - physiology</subject><subject>Cell Size</subject><subject>Field of view</subject><subject>Humans</subject><subject>Image contrast</subject><subject>Image detection</subject><subject>Image processing</subject><subject>Image segmentation</subject><subject>International conferences</subject><subject>Light</subject><subject>Light intensity</subject><subject>Luminous intensity</subject><subject>Mechanical engineering</subject><subject>Medical imaging</subject><subject>Methods</subject><subject>Microscopy</subject><subject>Microscopy, Phase-Contrast - methods</subject><subject>Morphology</subject><subject>Phase contrast</subject><subject>Reproducibility of Results</subject><subject>Robotics</subject><subject>Segmentation</subject><subject>Therapeutic applications</subject><subject>Time-Lapse Imaging - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Wang, Yuliang</au><au>Zhang, Zaicheng</au><au>Wang, Huimin</au><au>Bi, Shusheng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2015-06-12</date><risdate>2015</risdate><volume>10</volume><issue>6</issue><spage>e0130178</spage><epage>e0130178</epage><pages>e0130178-e0130178</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Cell image segmentation plays a central role in numerous biology studies and clinical applications. As a result, the development of cell image segmentation algorithms with high robustness and accuracy is attracting more and more attention. In this study, an automated cell image segmentation algorithm is developed to get improved cell image segmentation with respect to cell boundary detection and segmentation of the clustered cells for all cells in the field of view in negative phase contrast images. A new method which combines the thresholding method and edge based active contour method was proposed to optimize cell boundary detection. In order to segment clustered cells, the geographic peaks of cell light intensity were utilized to detect numbers and locations of the clustered cells. In this paper, the working principles of the algorithms are described. The influence of parameters in cell boundary detection and the selection of the threshold value on the final segmentation results are investigated. 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| subjects | Algorithms Biological effects Boundaries Cell Count Cell Line Cell Shape - physiology Cell Size Field of view Humans Image contrast Image detection Image processing Image segmentation International conferences Light Light intensity Luminous intensity Mechanical engineering Medical imaging Methods Microscopy Microscopy, Phase-Contrast - methods Morphology Phase contrast Reproducibility of Results Robotics Segmentation Therapeutic applications Time-Lapse Imaging - methods |
| title | Segmentation of the Clustered Cells with Optimized Boundary Detection in Negative Phase Contrast Images |
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