Paper
24 February 2012 Measurement of glucose concentration by image processing of thin film slides
Sankaranaryanan Piramanayagam, Eli Saber, David Heavner
Author Affiliations +
Abstract
Measurement of glucose concentration is important for diagnosis and treatment of diabetes mellitus and other medical conditions. This paper describes a novel image-processing based approach for measuring glucose concentration. A fluid drop (patient sample) is placed on a thin film slide. Glucose, present in the sample, reacts with reagents on the slide to produce a color dye. The color intensity of the dye formed varies with glucose at different concentration levels. Current methods use spectrophotometry to determine the glucose level of the sample. Our proposed algorithm uses an image of the slide, captured at a specific wavelength, to automatically determine glucose concentration. The algorithm consists of two phases: training and testing. Training datasets consist of images at different concentration levels. The dye-occupied image region is first segmented using a Hough based technique and then an intensity based feature is calculated from the segmented region. Subsequently, a mathematical model that describes a relationship between the generated feature values and the given concentrations is obtained. During testing, the dye region of a test slide image is segmented followed by feature extraction. These two initial steps are similar to those done in training. However, in the final step, the algorithm uses the model (feature vs. concentration) obtained from the training and feature generated from test image to predict the unknown concentration. The performance of the image-based analysis was compared with that of a standard glucose analyzer.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sankaranaryanan Piramanayagam, Eli Saber, and David Heavner "Measurement of glucose concentration by image processing of thin film slides", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83144U (24 February 2012); https://doi.org/10.1117/12.910978
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KEYWORDS
Glucose

Image segmentation

Calibration

Image processing algorithms and systems

Image processing

Image filtering

Thin films

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