Document Type
Article
Publication Date
2024
Abstract
Microscopy image analysis plays a crucial role in understanding cellular behavior anduncovering important insights in various biological and medical research domains.Tracking cells within the time-lapse microscopy images is a fundamental techniquethat enables the study of cell dynamics, interactions, and migration. While manual celltracking is possible, it is time-consuming and prone to subjective biases that impactresults. In order to solve this issue, we sought to create an automated software solu-tion, named cell analyzer, which is able to track cells within microscopy images withminimal input required from the user. The program of cell analyzer was written inPython utilizing the open source computer vision (OpenCV) library and featured agraphical user interface that makes it easy for users to access. The functions of allcodes were verified through closeness, area, centroid, contrast, variance, and celltracking test. Cell analyzer primarily utilizes image preprocessing and edge detectiontechniques to isolate cell boundaries for detection and analysis. It uniquely recordedthe area, displacement, speed, size, and direction of detected cell objects and visual-ized the data collected automatically for fast analysis. Our cell analyzer provides aneasy-to-use tool through a graphical user interface for tracking cell motion and ana-lyzing quantitative cell images..
Recommended Citation
Orr, J. Walker; McAfee, Lillian; Heath, Zach; Hozi, Marvin; and Kang, Young Bok Abraham, "The Development of an Automated Microscope Image Tracking and Analysis System" (2024). Faculty Publications - Department of Electrical Engineering and Computer Science. 48.
https://digitalcommons.georgefox.edu/eecs_fac/48
Comments
Originally published in McAfee L, Heath Z, Anderson W, Hozi M, Orr JW, Kang YA. The development of an automated microscope image tracking and analysis system. Biotechnol Prog. 2024 Nov-Dec;40(6):e3490. doi: 10.1002/btpr.3490. Epub 2024 Jun 18. PMID: 38888043.