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 Hüsnü Sazli | |
| dc.contributor.author | Osman Kalender | |
| dc.contributor.author | Yavuz Ege | |
| dc.contributor.author | Ercan, Tuncay | |
| dc.contributor.author | Ege, Yavuz | |
| dc.contributor.author | Karacor, Deniz | |
| dc.contributor.author | Nazlibilek, Sedat | |
| dc.contributor.author | Kalender, Osman | |
| dc.contributor.author | Sazli, Murat Husnu | |
| dc.date.accessioned | 2025-10-06T17:52:37Z | |
| 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. © 2014 Elsevier Ltd. All rights reserved. © 2017 Elsevier B.V. All rights reserved. | |
| dc.identifier.doi | 10.1016/j.measurement.2014.04.008 | |
| dc.identifier.issn | 02632241 | |
| dc.identifier.issn | 0263-2241 | |
| dc.identifier.issn | 1873-412X | |
| dc.identifier.scopus | 2-s2.0-84901417604 | |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84901417604&doi=10.1016%2Fj.measurement.2014.04.008&partnerID=40&md5=22b985dba7875aad51cc7ab28f4cf924 | |
| dc.identifier.uri | https://gcris.yasar.edu.tr/handle/123456789/10039 | |
| dc.identifier.uri | https://doi.org/10.1016/j.measurement.2014.04.008 | |
| dc.language.iso | English | |
| dc.publisher | Elsevier B.V. | |
| dc.relation.ispartof | Measurement | |
| dc.rights | info:eu-repo/semantics/closedAccess | |
| dc.source | Measurement: Journal of the International Measurement Confederation | |
| dc.subject | Automatic Counting, Neural Network, Principal Component Analysis (pca), White Blood Cells, Automation, Blood, Cells, Neural Networks, Principal Component Analysis, Automated Systems, Automatic Counting, Automatic Segmentations, Blood Samples, Classification Process, Cost Effective, Manual Techniques, White Blood Cells, Diagnosis | |
| dc.subject | Automation, Blood, Cells, Neural networks, Principal component analysis, Automated systems, Automatic counting, Automatic segmentations, Blood samples, Classification process, Cost effective, Manual techniques, White blood cells, Diagnosis | |
| dc.subject | White Blood Cells | |
| dc.subject | Automatic Counting | |
| dc.subject | Neural Network | |
| dc.subject | Principal Component Analysis (PCA) | |
| dc.title | Automatic segmentation counting size determination and classification of white blood cells | |
| dc.type | Article | |
| dspace.entity.type | Publication | |
| gdc.author.id | Ercan, Tuncay/0000-0003-0014-5106 | |
| gdc.author.id | Karacor, Deniz/0000-0001-6961-8966 | |
| gdc.author.id | SAZLI, Murat Hüsnü/0000-0001-9235-3679 | |
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| gdc.author.scopusid | 19638410900 | |
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| gdc.author.wosid | Ege, Yavuz/AAD-7800-2019 | |
| gdc.author.wosid | Karacor, Deniz/AAH-3088-2020 | |
| gdc.author.wosid | SAZLI, Murat Hüsnü/AAH-6663-2020 | |
| gdc.author.wosid | Ercan, Tuncay/F-9938-2011 | |
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| gdc.description.department | ||
| gdc.description.departmenttemp | [Nazlibilek, Sedat] Atilim Univ, Fac Engn, Dept Mechatron Engn, TR-06800 Ankara, Turkey; [Karacor, Deniz; Sazli, Murat Husnu] Ankara Univ, Fac Engn, Dept Elect Engn, TR-06100 Ankara, Turkey; [Ercan, Tuncay] Yasar Univ, Fac Engn, Dept Comp Engn, Izmir, Turkey; [Kalender, Osman] Bursa Orhangazi Univ, Fac Engn, Dept Elect Elect Engn, TR-16350 Bursa, Turkey; [Ege, Yavuz] Balikesir Univ, Dept Phys, Necatibey Fac Educ, TR-10100 Balikesir, Turkey | |
| gdc.description.endpage | 65 | |
| gdc.description.publicationcategory | Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı | |
| gdc.description.startpage | 58 | |
| gdc.description.volume | 55 | |
| gdc.description.woscitationindex | Science Citation Index Expanded | |
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| 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) | |
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| gdc.virtual.author | Ercan, Ahmet Tuncay | |
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| person.identifier.scopus-author-id | Nazlibilek- Sedat (24473589800), Karacor- Deniz (54909245800), Ercan- Tuncay (21933416500), Sazli- Murat Hüsnü (15078749000), Kalender- Osman (19639054500), Ege- Yavuz (19638410900) | |
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