No More Eye Sore! Automated Cell Counting Using ImageJ
Additional Funding Sources
The project described was supported by the University of Idaho, College of Agricultural and Life Sciences.
Presentation Date
7-2019
Abstract
Biological research often requires the counting of cells for a myriad of reasons such as determining the concentration of specific cells, pathogen infectivity, chemical sensitivity and others. Traditionally, this is done manually by a trained microscopist at the expense of significant time, money, and effort of the lab and lab personnel. To address this situation for analysis of blood stage malaria parasite infection, I created an automated solution using the program ImageJ to quickly, accurately, and effectively differentiate infected red blood cells with ~98% accuracy when tested on images of stained peripheral blood reticulocytes, mature red blood cells, and white blood cells through batch processing. The program was also used to create an output stream of images that visually display the computer’s cell guesses for easy verification of accuracy. In brief, the macro created from the tools in ImageJ coverts a color image to an 8-bit image and applies different ImageJ manipulation functions, such as Threshold and Watershed algorithms, to isolate cells from one another and count the cells when all manipulation steps are completed.
No More Eye Sore! Automated Cell Counting Using ImageJ
Biological research often requires the counting of cells for a myriad of reasons such as determining the concentration of specific cells, pathogen infectivity, chemical sensitivity and others. Traditionally, this is done manually by a trained microscopist at the expense of significant time, money, and effort of the lab and lab personnel. To address this situation for analysis of blood stage malaria parasite infection, I created an automated solution using the program ImageJ to quickly, accurately, and effectively differentiate infected red blood cells with ~98% accuracy when tested on images of stained peripheral blood reticulocytes, mature red blood cells, and white blood cells through batch processing. The program was also used to create an output stream of images that visually display the computer’s cell guesses for easy verification of accuracy. In brief, the macro created from the tools in ImageJ coverts a color image to an 8-bit image and applies different ImageJ manipulation functions, such as Threshold and Watershed algorithms, to isolate cells from one another and count the cells when all manipulation steps are completed.
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