Development of an automated cell culture monitoring image analysis algorithm

Abstract

Cytometer is used to identify and photograph the different particles present in the sample and ideally, the cells will pass one after the other through the laser beam and also the light reflected is indicative of the cells and their components. Thousands of cells can be studied in a short time with this flow cytometry technology. However, it has to be measured and collected manually. Manual labelling requires the user to have the requisite experience to perform the detection process and to quantify the samples individually. Repetition of the whole procedure can contribute to error, which may affect the precision of the data and the effectiveness of the measuring procedure. Image analysis has emerged as a powerful method for evaluating different parameters of cell biology in an unparalleled and highly precise way. The software available is appropriate for processing of fluorescence and phase contrast images, but does not always yield good results from the transmission of light microscopy images, as the inherent variance in the extraction of the image process used, such as adjusting light intensity or contrast. This paper introduces an image processing algorithm that analyzes cell growth and is able to measure the total number of cells in the image using MATLAB image processing techniques. With this algorithm, the characteristics of the cell image will be calculated automatically and applied to the excel file for further use. Following the design of the software, the built program eliminates the drawback of manual calculation and also increases data accuracy.