Automatic segmentation- counting- size determination and classification of white blood cells

dc.contributor.author Sedat Nazlibilek
dc.contributor.author Deniz Karacor
dc.contributor.author Tuncay Ercan
dc.contributor.author Murat Husnu Sazli
dc.contributor.author Osman Kalender
dc.contributor.author Yavuz Ege
dc.date SEP
dc.date.accessioned 2025-10-06T16:23:30Z
dc.date.issued 2014
dc.description.abstract The counts the so-called differential counts and sizes of different types of white blood cells provide invaluable information to evaluate a wide range of important hematic pathologies from infections to leukemia. Today the diagnosis of diseases can still be achieved mainly by manual techniques. However this traditional method is very tedious and time-consuming. The accuracy of it depends on the operator's expertise. There are laser based cytometers used in laboratories. These advanced devices are costly and requires accurate hardware calibration. They also use actual blood samples. Thus there is always a need for a cost effective and robust automated system. The proposed system in this paper automatically counts the white blood cells determine their sizes accurately and classifies them into five types such as basophil lymphocyte neutrophil monocyte and eosinophil. The aim of the system is to help for diagnosing diseases. In our work a new and completely automatic counting segmentation and classification process is developed. The outputs of the system are the number of white blood cells their sizes and types. (C) 2014 Elsevier Ltd. All rights reserved.
dc.identifier.doi 10.1016/j.measurement.2014.04.008
dc.identifier.issn 0263-2241
dc.identifier.uri http://dx.doi.org/10.1016/j.measurement.2014.04.008
dc.identifier.uri https://gcris.yasar.edu.tr/handle/123456789/7873
dc.language.iso English
dc.publisher ELSEVIER SCI LTD
dc.relation.ispartof Measurement
dc.source MEASUREMENT
dc.subject White blood cells, Neural network, Automatic counting, Principal Component Analysis (PCA)
dc.subject IDENTIFICATION
dc.title Automatic segmentation- counting- size determination and classification of white blood cells
dc.type Article
dspace.entity.type Publication
gdc.bip.impulseclass C4
gdc.bip.influenceclass C3
gdc.bip.popularityclass C3
gdc.coar.type text::journal::journal article
gdc.collaboration.industrial false
gdc.description.endpage 65
gdc.description.startpage 58
gdc.description.volume 55
gdc.identifier.openalex W2012442592
gdc.index.type WoS
gdc.oaire.diamondjournal false
gdc.oaire.impulse 24.0
gdc.oaire.influence 1.5751853E-8
gdc.oaire.isgreen true
gdc.oaire.keywords White Blood Cells
gdc.oaire.keywords Neural Network
gdc.oaire.keywords Automatic Counting
gdc.oaire.keywords 006
gdc.oaire.keywords Principal Component Analysis (PCA)
gdc.oaire.popularity 6.995558E-8
gdc.oaire.publicfunded false
gdc.oaire.sciencefields 0209 industrial biotechnology
gdc.oaire.sciencefields 0202 electrical engineering, electronic engineering, information engineering
gdc.oaire.sciencefields 02 engineering and technology
gdc.openalex.collaboration National
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gdc.openalex.normalizedpercentile 0.98
gdc.openalex.toppercent TOP 10%
gdc.opencitations.count 133
gdc.plumx.crossrefcites 126
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gdc.plumx.mendeley 139
gdc.plumx.newscount 1
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oaire.citation.endPage 65
oaire.citation.startPage 58
person.identifier.orcid SAZLI- Murat Husnu/0000-0001-9235-3679, Ercan- Tuncay/0000-0003-0014-5106, Karacor- Deniz/0000-0001-6961-8966
publicationvolume.volumeNumber 55
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