Automatic Segmentation, Counting, Size Determination and Classification of White Blood Cells
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Date
2014
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Sci Ltd
Open Access Color
Green Open Access
Yes
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Publicly Funded
No
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.
Description
Sazli, Murat/0000-0001-9235-3679; Karacor, Deniz/0000-0001-6961-8966; Ercan, Tuncay/0000-0003-0014-5106;
Keywords
White blood cells, Neural network, Automatic counting, Principal Component Analysis (PCA), White Blood Cells, Neural Network, Automatic Counting, 006, Principal Component Analysis (PCA)
Turkish CoHE Thesis Center URL
Fields of Science
0209 industrial biotechnology, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology
Citation
WoS Q
Q1
Scopus Q

OpenCitations Citation Count
125
Source
Measurement
Volume
55
Issue
Start Page
58
End Page
65
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CrossRef : 126
Scopus : 158
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Mendeley Readers : 135
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8.43995407
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