Machine reading and recovery of colors for hemoglobin-related bioassays and bioimaging

Abstract

Despite advances in machine learning and computer vision for biomedical imaging, machine reading and learning of colors remain underexplored. Color consistency in computer vision, color constancy in human perception, and color accuracy in biomedical imaging are intertwined, complicating digital color-based diagnostics. Existing color reference charts and correction algorithms are inadequate for mobile health (mHealth) and telemedicine in digital health applications where detecting subtle color changes is critical. We present a machine reading and learning platform for color recognition and quantification to extract diagnostic information from colors. A unique combination of spectroscopic gamut determination, reference color optimization, nonsubjective quantification metrics, and neural network-based color recovery retrieves absolute colors of biological tissue. Studies on inflammation bioimaging of photocarcinogenesis and mHealth blood hemoglobin assessment demonstrate accuracy and precision in color recovery across diverse acquisition scenarios. The reported framework overcomes limitations of conventional colorimetric detection, enabling machine-compatible color-based bioassays and bioimaging, advancing digital diagnostics.