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Computerized mouse pupil size measurement for pupillary light reflex analysis.

Wei Lu¹, Jinglu Tan, Keqing Zhang

¹Department of Biological Engineering, University of Missouri, Columbia, MO 65211, USA.

Computer Methods and Programs in Biomedicine

|February 20, 2008

Summary

This study presents a computerized method for accurate pupil size measurement, crucial for pupillary light reflex (PLR) analysis. The novel approach offers consistent and reliable results, outperforming manual measurements in clinical research.

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Area of Science:

Ophthalmology
Computer Vision
Medical Imaging

Background:

Accurate pupil size measurement is vital for pupillary light reflex (PLR) analysis in clinical diagnosis and vision research.
Automated pupil detection faces challenges due to low contrast, reflections, artifacts, and noise in infrared eye imaging.

Purpose of the Study:

To develop and evaluate a computerized method for reliable pupil detection and size measurement.
To address the limitations of existing automated methods in challenging eye imaging conditions.

Main Methods:

A region-growing algorithm was used for initial image segmentation.
An iterative randomized Hough transform (IRHT) with an elliptical model was employed for pupil detection.
The IRHT method was designed to iteratively suppress noise and extraneous structures for robust measurements.

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Main Results:

The computerized method achieved a mean absolute difference of 3.84% compared to manual measurements across 72 images.
The inter-run variation of the computerized method was significantly lower (1.24%) than the inter-observer variation of manual methods (7.45%).
The results indicate a higher level of consistency and reliability with the automated approach.

Conclusions:

The developed computerized method provides accurate and consistent pupil size measurements.
This automated technique can significantly aid pupillary light reflex (PLR) analysis and other non-invasive functional vision tests.
The method offers a reliable alternative to manual measurements, improving efficiency and reducing variability in clinical and research settings.