Özkan, Akın

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Profile URL
https://hdl.handle.net/20.500.14411/7674
Name Variants
Ö.,Akın
Özkan,A.
Akin, Ozkan
Akın, Özkan
A.,Özkan
Ozkan,A.
A.,Ozkan
O.,Akin
O., Akin
A., Ozkan
Özkan, Akın
Ozkan, Akin
Job Title
Araştırma Görevlisi
Email Address
akin.ozkan@atilim.edu.tr
Main Affiliation
Department of Electrical & Electronics Engineering
Status
Former Staff
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WoS Researcher ID

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5

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0

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14

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10

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3

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9

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16

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Scholarly Output

10

Articles

3

Views / Downloads

2/0

Supervised MSc Theses

1

Supervised PhD Theses

1

WoS Citation Count

26

Scopus Citation Count

38

WoS h-index

2

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3

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0

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0

WoS Citations per Publication

2.60

Scopus Citations per Publication

3.80

Open Access Source

4

Supervised Theses

2

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JournalCount
2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011 -- 2011 IEEE 19th Signal Processing and Communications Applications Conference, SIU 2011 -- 20 April 2011 through 22 April 2011 -- Antalya -- 855281
2016 24th Signal Processing and Communication Application Conference, SIU 2016 - Proceedings -- 24th Signal Processing and Communication Application Conference, SIU 2016 -- 16 May 2016 through 19 May 2016 -- Zonguldak -- 1226051
24th Signal Processing and Communication Application Conference (SIU) -- MAY 16-19, 2016 -- Zonguldak, TURKEY1
25th IEEE International Conference on Image Processing (ICIP) -- OCT 07-10, 2018 -- Athens, GREECE1
Biomedical Research (India)1
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Author: Özkan,A.End date: 2019

Scholarly Output Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Article
    Citation - Scopus: 1
    Computer Vision Based Automated Cell Counting Pipeline: a Case Study for Hl60 Cancer Cell on Hemocytometer
    (Scientific Publishers of India, 2018) Özkan,A.; İşgör,S.B.; Şengül,G.; İşgör,Y.G.
    Counting of cells can give useful information about the cell density to understand the concerning cell culture condition. Usually, cell counting can be achieved manually with the help of the microscope and hemocytometer by the domain experts. The main drawback of the manual counting procedure is that the reliability highly depends on the experience and concentration of the examiners. Therefore, computer vision based automated cell counting is an essential tool to improve the accuracy. Although the commercial automated cell counting systems are available in the literature, their high cost limits their broader usage. In this study, we present a cell counting pipeline for light microscope images based on hemocytometer that can be easily adapted to the various cell types. The proposed method is robust to adverse image and cell culture conditions such as cell shape deformations, lightning conditions and brightness differences. In addition, we collect a novel human promyelocytic leukemia (HL60) cancer cell dataset to test our pipeline. The experimental results are presented in three measures: recall, precision and F-measure. The method reaches up to 98%, 92%, and 95% based on these three measures respectively by combining Support Vector Machine (SVM) and Histogram of Oriented Gradient (HOG). © 2018, Scientific Publishers of India. All rights reserved.
  • Thumbnail Image
    Conference Object
    Citation - Scopus: 3
    An Alternative Method for Cell Counting;
    (2011) Özkan,A.; Belgin Işgör,S.; Tora,H.; Uyar,P.; Işcan,M.
    Cell counts and classification of the cells play an important role in the field of microbiology and cell biology. Although there exists many counting processes for cells of interest in suspension, the most basic cell counting process is performed by a person via the microscope. For counting cells the simplest, widely used and the most economic method is the use of hemocytometer counting. In this study, the hemocytometer counting was used but the the cells were counted by a proposed image based approach. The developed technique herein uses neural network along with the Hough transform. © 2011 IEEE.