Robust Automated Image Analysis of Activated Red Blood Cells
Jan Martens1, *, Mauro C. Wesseling3, Joachim Weickert2, Ingolf Bernhardt3
Identifiers and Pagination:Year: 2016
First Page: 22
Last Page: 33
Publisher Id: BIOLSCI-2-22
Article History:Received Date: 04/03/2016
Revision Received Date: 06/10/2016
Acceptance Date: 07/10/2016
Electronic publication date: 30/11/2016
Collection year: 2016
open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
The investigation of eryptosis, a process in red blood cells (RBCs) comparable to apoptosis, has medical importance due to a link to thrombosis. Eryptosis is indicated by an exposure of phosphatidylserine (PS) on the outer cellular membrane. Experimental data suggests a relation between an elevated intracellular Ca2+ content of RBCs and PS exposure.
To investigate this relation in wet-lab experiments, live cell imaging with fluorescence microscopy was carried out. RBCs from blood samples were labeled with fluorescence dyes to either mark exposed PS or intracellular Ca2+. Manual analysis requires the experimenter to count all cells in these images and to classify them according to their activation states and shape changes.
A combination of well-established image analysis techniques allows us to automate this task. A preprocessing step consisting of bandpass filtering and median filtering prepares the image such that a gradient operator and Otsu thresholding can extract cell boundaries. Followed by this, a Hough transform is applied to the preprocessed image to extract the cells. The activation state of detected RBCs is classified with a thresholding of the ratio between intracellular and outercellular brightness. Measuring and thresholding intracellular fluctuations allows to classify cells into discocytes and echinocytes. With these techniques we yield robust results, while saving valuable time.
Results and Conclusion:
Our results show that the automated system detects cells with high reliability and that the classifications are comparable to manual classifications.