Extraction of biological parameters of wound-healing processes from time-lapse videos

Bachelor thesis (2019)

Authors

D.S. Tsang Electrical Engineering, Mathematics and Computer Science

Contributors

N.V. Budko Numerical Analysis - EEMCS (mentor)
F.J. Vermolen Numerical Analysis - EEMCS (mentor)
E.M. van Elderen Mathematical Physics - EEMCS (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2019 Daniel Tsang
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Published Date

05-07-2019

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Experimental data have been extracted from wound-healing time-lapse videos. These data include the detection of the area of the wound and the detection of individual cells. Extracting the area of the wound is done by using a function in Python OpenCV called cv2.findcontours. An improved method to extract the area of the wound is introduced as well using the Sobel filter. Detecting the individual cells is done by localizing the local maxima in an image. Histogram equalization is applied to enhance the global contrast to increase the performance of cell detection. The continuum model is applied in one of the videos, where we used different parameters to model the data. A method is described to nd the optimal parameter of the continuum model by using the method of least-squares. Finally, two methods using the kernel density estimation are described which can be useful in future studies.