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Published on: March 24, 2020

An automatic screening method for strabismus detection based on image processing.

Xilang Huang¹, Sang Joon Lee², Chang Zoo Kim^2,3

¹Department of Artificial Intelligent Convergence, Pukyong National University, Busan, Korea.

|August 3, 2021

Summary

An automated method for strabismus screening was developed using facial images. This technique offers a promising solution for remote areas lacking medical access, improving early detection of eye conditions.

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Area of Science:

Ophthalmology
Computer Vision
Medical Imaging

Background:

Strabismus screening is crucial for early detection and treatment of eye conditions.
Limited access to medical facilities in remote areas hinders timely diagnosis.
Automated screening methods can bridge this accessibility gap.

Purpose of the Study:

To develop an automated strabismus screening method.
To provide a tool for individuals in remote areas with poor medical accessibility.
To enable early detection of strabismus through image analysis.

Main Methods:

Utilized a convolutional neural network for face and landmark detection.
Applied Otsu's binarization and HSV color model to isolate the eye region.
Calculated pupil center coordinates and limbus-to-canthus distances for deviation measurement.

Main Results:

The method demonstrated significant differences in iris positional similarity between normal and strabismus images (p<0.001).
Positional similarity values were 1.073 ± 0.014 for normal and 1.924 ± 0.169 for strabismus images.
Validation on 60 images (30 normal, 30 strabismus) confirmed the method's effectiveness.

Conclusions:

The proposed automated method is a promising tool for strabismus screening.
It offers a viable solution for populations in remote or underserved areas.
Facilitates accessible and early detection of strabismus.