Authors: Samer I. Awad; Rula G. Abdallat; Othman A. Smadi; Thakir D. Al-Momani
Addresses: The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan ' The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan ' The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan ' The Hashemite University, P.O. Box 330127, Zarqa 13133, Jordan
Abstract: The assessment of cell count is essential for the evaluation of biological cell proliferation development. Manual counting can be time consuming and subject to human error as it depends on visual inspection. On the other hand, automated counting using software based morphological analysis can eliminate or reduce these disadvantages and provide statistical reliability. In this study, we employ a software-based method for the automated counting of mesenchymal stem cells (MSCs) proliferation in the bone callus of Wistar rats to evaluate fracture healing. The proposed method started with extracting the green component of the digital image acquired using a light microscope. The subsequent stages involved: contrast enhancement, adaptive thresholding and false detection reduction. This method was tested using 48 MSCs images and the results were evaluated by a specialist. The average of precision, recall and F-measure were found to be 87.14%, 88.04% and 87.50% respectively.
Keywords: automated cell counting; biological cell counting; image processing; image segmentation; pattern recognition; automated thresholding; light microscopic images; mesenchymal stem cells; MSC; false detection minimisation.
International Journal of Computational Vision and Robotics, 2019 Vol.9 No.1, pp.1 - 13
Received: 09 Jan 2018
Accepted: 01 May 2018
Published online: 21 Feb 2019 *