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

Research Projects

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Organizational Unit
Department of Mechatronics Engineering
Our purpose in the program is to educate our students for contributing to universal knowledge by doing research on contemporary mechatronics engineering problems and provide them with design, production and publication skills. To reach this goal our post graduate students are offered courses in various areas of mechatronics engineering, encouraged to do research to develop their expertise and their creative side, as well as develop analysis and design skills.

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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)

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Citation

116

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Source

Volume

55

Issue

Start Page

58

End Page

65

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