Computer vision algorithm to extract color data of pixels in microfluidic paper based analytical devices
Date
2021
Authors
Deotale, Saurabh, author
Beveridge, James Ross, advisor
Blanchard, Nathaniel, committee member
Henry, Charles, committee member
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Abstract
Microfludic paper-based devices are fast becoming an inexpensive and faster option than traditional methods for substance detection and chemical measurements. These devices are designed to be used in the field for quicker result. One hurdle towards that goal is a manual step of data extraction from the images of these devices for further analysis and results. This involves identifying and extracting color data from specific regions of interest. The color data is the color values in BGR and HSV color channels of the pixels lying in these regions of interest. The manual demands labor and time that can avoided by automating this process using computer vision techniques. The goal of this thesis is to aid chemists by automating the data extraction process. This thesis presents a layered algorithm which uses simple techniques like region growing and thresholding in conjunction with leveraging the knowledge of the device design to extract the required data. This data is then labeled and compiled in CSV file for further analysis.
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Subject
data extraction
region growing
microfludic paper-based devices
computer vision algorithm