March 6, 2023
Society of Photo-Optical Instrumentation Engineers

Evaluating color correction algorithms for automated interpretation of urinalysis dipsticks with low-cost image sensors

Wenbo Wang, James W. Stafford, Andrew Miller, Rose M. Buchmann, Ethan Spencer, Angela Michelle T. San Juan, Jamie Purcell, and Matthew D. Keller

Some medical diagnostic tools, such as urinalysis dipsticks, rely on reading colors accurately for making clinical decisions. Reading color visually can be subject to 1) perceptual differences (inter-observer), 2) environmental factors such as illumination, and 3) target coloring (metamerism), especially among users with limited training and experience. Mobile phone cameras and compact camera modules offer potential low-cost platforms for automated, objective color readouts. However, image colors are a function of camera sensor and illumination characteristics. To restore color fidelity, color correction techniques must be applied to account for systematic deviation. This work aims to provide a quantitative assessment of color correction techniques and reduce variability in color interpretation for urinalysis dipstick results using a low-cost imager. Three color correction methods – linear, polynomial, and root-polynomial regression – were compared for performance in color difference reduction. A standard color checker card was used as reference to compute color correction matrices. A custom imaging system with a low-cost camera module was developed to capture images under controlled illumination. Reference values of the color checker card were obtained with a CM-26d handheld spectrophotometer. The CIE2000 ∆E was used to quantify the color difference between the camera image and the spectrometer to evaluate 3 color correction algorithms. The derived color correction matrices were applied to urinalysis dipstick images and compared to the spectrometer readings. Results indicated that polynomial fitting showed the lowest ∆E during calibration but failed to properly correct urine dipstick colors. Root polynomial offered the best performance in reducing color differences to be below 3 to 4 ∆E. Utilizing L*a*b values for classifying a given dipstick result according to reference concentration levels, it was found that quadratic discriminant analysis (QDA) and k Nearest Neighbor (kNN) classifiers achieved an 82.9% and 97.1% accuracy, respectively.

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