Automatic segmentation counting size determination and classification of white blood cells

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Date

2014

Authors

Sedat Nazlibilek
Deniz Karacor
Tuncay Ercan
Murat Hüsnü Sazli
Osman Kalender
Yavuz Ege

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier B.V.

Open Access Color

Green Open Access

Yes

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Publicly Funded

No
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Top 10%
Influence
Top 1%
Popularity
Top 1%

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Journal Issue

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.

Description

Keywords

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, 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, White Blood Cells, Automatic Counting, Neural Network, Principal Component Analysis (PCA), White Blood Cells, Neural Network, Automatic Counting, 006, Principal Component Analysis (PCA)

Fields of Science

0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

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OpenCitations Citation Count
133

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Volume

55

Issue

Start Page

58

End Page

65
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CrossRef : 126

Scopus : 163

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Mendeley Readers : 139

SCOPUS™ Citations

163

checked on Apr 09, 2026

Web of Science™ Citations

128

checked on Apr 09, 2026

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8.6467

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GOOD HEALTH AND WELL-BEING3
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