Computer Vision Based Automated Cell Counting Pipeline: a Case Study for Hl60 Cancer Cell on Hemocytometer

dc.authorscopusid 43261651300
dc.authorscopusid 57190741107
dc.authorscopusid 8402817900
dc.authorscopusid 7801688219
dc.contributor.author Özkan,A.
dc.contributor.author İşgör,S.B.
dc.contributor.author Şengül,G.
dc.contributor.author İşgör,Y.G.
dc.contributor.other Department of Electrical & Electronics Engineering
dc.contributor.other Computer Engineering
dc.contributor.other Chemical Engineering
dc.date.accessioned 2024-07-05T15:45:17Z
dc.date.available 2024-07-05T15:45:17Z
dc.date.issued 2018
dc.department Atılım University en_US
dc.department-temp Özkan A., Department of Electrical and Electronics Engineering, Atilim University, Ankara, Turkey; İşgör S.B., Department of Chemical Engineering and Applied Chemistry, Atilim University, Ankara, Turkey; Şengül G., Department of Computer Engineering, Atilim University, Ankara, Turkey; İşgör Y.G., Medical Laboratory Techniques, Ankara University Vocational School of Health, Ankara, Turkey en_US
dc.description.abstract Counting of cells can give useful information about the cell density to understand the concerning cell culture condition. Usually, cell counting can be achieved manually with the help of the microscope and hemocytometer by the domain experts. The main drawback of the manual counting procedure is that the reliability highly depends on the experience and concentration of the examiners. Therefore, computer vision based automated cell counting is an essential tool to improve the accuracy. Although the commercial automated cell counting systems are available in the literature, their high cost limits their broader usage. In this study, we present a cell counting pipeline for light microscope images based on hemocytometer that can be easily adapted to the various cell types. The proposed method is robust to adverse image and cell culture conditions such as cell shape deformations, lightning conditions and brightness differences. In addition, we collect a novel human promyelocytic leukemia (HL60) cancer cell dataset to test our pipeline. The experimental results are presented in three measures: recall, precision and F-measure. The method reaches up to 98%, 92%, and 95% based on these three measures respectively by combining Support Vector Machine (SVM) and Histogram of Oriented Gradient (HOG). © 2018, Scientific Publishers of India. All rights reserved. en_US
dc.identifier.citationcount 1
dc.identifier.doi 10.4066/biomedicalresearch.29-18-575
dc.identifier.endpage 2962 en_US
dc.identifier.issn 0970-938X
dc.identifier.issue 14 en_US
dc.identifier.scopus 2-s2.0-85052730669
dc.identifier.startpage 2956 en_US
dc.identifier.uri https://doi.org/10.4066/biomedicalresearch.29-18-575
dc.identifier.uri https://hdl.handle.net/20.500.14411/3893
dc.identifier.volume 29 en_US
dc.institutionauthor Özkan, Akın
dc.institutionauthor Şengül, Gökhan
dc.institutionauthor İşgör, Sultan Belgin
dc.language.iso en en_US
dc.publisher Scientific Publishers of India en_US
dc.relation.ispartof Biomedical Research (India) en_US
dc.relation.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.scopus.citedbyCount 1
dc.subject Cell counting en_US
dc.subject Hemocytometer en_US
dc.subject HL60 en_US
dc.subject Light microscope en_US
dc.subject Visual feature extraction en_US
dc.title Computer Vision Based Automated Cell Counting Pipeline: a Case Study for Hl60 Cancer Cell on Hemocytometer en_US
dc.type Article en_US
dspace.entity.type Publication
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