Deotale, Saurabh, authorBeveridge, James Ross, advisorBlanchard, Nathaniel, committee memberHenry, Charles, committee member2021-09-062021-09-062021https://hdl.handle.net/10217/233675Microfludic 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.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.data extractionregion growingmicrofludic paper-based devicescomputer vision algorithmText