Automated Quantification of Immunomagnetic Beads and Leukemia Cells from Optical Microscope Images

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Date

2019

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier Sci Ltd

Open Access Color

Green Open Access

Yes

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No
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Top 10%
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Abstract

Quantification of tumor cells is crucial for early detection and monitoring the progress of cancer. Several methods have been developed for detecting tumor cells. However, automated quantification of cells in the presence of immunomagnetic beads has not been studied. In this study, we developed computer vision based algorithms to quantify the leukemia cells captured and separated by micron size immunomagnetic beads. Color, size based object identification and machine learning based methods were implemented to quantify targets in the images recorded by a bright field microscope. Images acquired by a 40x or a 20x objective were analyzed, the immunomagnetic beads were detected with an error rate of 0.0171 and 0.0384 respectively. Our results reveal that the proposed method attains 91.6% precision for the 40x objective and 79.7% for the 20x objective. This algorithm has the potential to be the signal readout mechanism of a biochip for cell detection. (C) 2019 Elsevier Ltd. All rights reserved.

Description

Tasdemir, Kasim/0000-0003-4542-2728; Celebi, Fatma/0000-0001-7472-8297; Yilmaz, Bulent/0000-0003-2954-1217; Icoz, Kutay/0000-0002-0947-6166;

Keywords

Leukemia Cells, Image-Processing, Bright-Field Optical Microscopy, Machine Learning, Immunomagnetic Beads, Support Vector Machines

Fields of Science

0301 basic medicine, 03 medical and health sciences, 0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology

Citation

WoS Q

Q2

Scopus Q

Q1
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OpenCitations Citation Count
24

Source

Biomedical Signal Processing and Control

Volume

49

Issue

Start Page

473

End Page

482
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Citations

CrossRef : 2

Scopus : 27

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Mendeley Readers : 46

SCOPUS™ Citations

29

checked on Apr 20, 2026

Web of Science™ Citations

22

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Page Views

5

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Downloads

3

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4.5485

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