Browsing by Author "Özkan,A."
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Conference Object Citation Count: 3An alternative method for cell counting;(2011) Özkan, Akın; Tora, Hakan; Tora,H.; Uyar,P.; Işcan,M.; Airframe and Powerplant Maintenance; Department of Electrical & Electronics EngineeringCell 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.Article Citation Count: 1Computer vision based automated cell counting pipeline: A case study for HL60 cancer cell on hemocytometer(Scientific Publishers of India, 2018) Özkan, Akın; İşgör,S.B.; Şengül, Gökhan; İşgör,Y.G.; İşgör, Sultan Belgin; Department of Electrical & Electronics Engineering; Computer Engineering; Chemical EngineeringCounting 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.