Machine reading and recovery of colors for hemoglobin-related bioassays and bioimaging
Year: 2025
Type: article
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.
Source: Science Advances
Institutions: Purdue University West Lafayette, Kumoh National Institute of Technology, Indiana University School of Medicine, Indiana University Melvin and Bren Simon Comprehensive Cancer Center, Indiana University, Richard L. Roudebush VA Medical Center, Indiana University Indianapolis, Purdue University Institute for Cancer Research
Language: English
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,500
OpenAlex ID: W4411078250