Analysis of the Consensual Pupillary Reflex Using Blue LED Step Light and Automated Image Segmentation

This study evaluates the dynamics of the human pupillary reflex in response to a stepped blue light stimulus (465 nm) in young adults residing at high altitude (3400 m above sea level). High-resolution video sequences of three participants were analyzed using four classical image segmentation techniques: K-Means, Otsu, fixed binary threshold, and multi-channel RGB threshold. Rather than proposing new algorithms, this work evaluates the technical feasibility and stability of computationally lightweight segmentation approaches under controlled lighting conditions and with low-cost hardware constraints. Among the methods evaluated, fixed binary thresholding showed stable temporal behavior and minimal computational complexity within the experimental setup. The results show a consistent contraction–plateau–recovery pattern across all participants, with representative contraction, stabilization, and recovery times of 1.89 s, 0.41 s, and 2.33 s, respectively. Although limited by the small sample size, these findings support the feasibility of implementing simplified segmentation strategies for pupillometry in resource-limited settings.