Purpose: To show feasibility of computerized techniques for ocular redness quantification in clinical studies, and to propose an automatic, objective method.
Methods: Software for quantification of redness of the bulbar conjunctiva was developed. It provides an interface for manual and automatic sclera segmentation along with automated alignment of region of interest to enable estimation of changes in redness. The software also includes the redness scoring methods: (1) contrast-limited adaptive histogram equalization (CLAHE) in red-green-blue (RGB) color model, (2) product of saturation and hue in hue-saturation-value (HSV), and (3) average of angular sections in HSV. Our validation pipeline compares the scoring outcomes from the perspectives of segmentation reliability, segmentation precision, segmentation automation, and the choice of redness scoring methods.
Results: Ninety-two photographs of eyes before and after provoked redness were evaluated. Redness in manually segmented images was significantly different within human observers (interobserver, P = 0.04) and two scoring sessions (intraobserver, P < 0.001). Automated segmentation showed the smallest variability, and can therefore be seen as a robust segmentation method. The RGB-based scoring method was less sensitive in redness assessment.
Conclusions: Computation of ocular redness depends heavily on sclera segmentation. Manual segmentation appears to be subjective, resulting in systematic errors in intraobserver and interobserver settings. At the same time, automatic segmentation seems to be consistent. The scoring methods relying on HSV color space appeared to be more consistent.
Translational Relevance: Computerized quantification of ocular redness holds great promise to objectify ocular redness in the standard clinical care and, in particular, in clinical trials.