Automated anterior segment OCT image analysis for Angle Closure Glaucoma mechanisms classification

• A new method for automated classification of various mechanisms of angle closure disease.
• Compound image transform features are directly extracted from the raw AS-OCT images without any segmentation.
• Anterior chamber area measurement is not required in this method.
• Selected morphological features are used to achieve better classification result.

Background and objectivesAngle closure glaucoma (ACG) is an eye disease prevalent throughout the world. ACG is caused by four major mechanisms: exaggerated lens vault, pupil block, thick peripheral iris roll, and plateau iris. Identifying the specific mechanism in a given patient is important because each mechanism requires a specific medication and treatment regimen. Traditional methods of classifying these four mechanisms are based on clinically important parameters measured from anterior segment optical coherence tomography (AS-OCT) images, which rely on accurate segmentation of the AS-OCT image and identification of the scleral spur in the segmented AS-OCT images by clinicians.MethodsIn this work, a fully automated method of classifying different ACG mechanisms based on AS-OCT images is proposed. Since the manual diagnosis mainly based on the morphology of each mechanism, in this study, a complete set of morphological features is extracted directly from raw AS-OCT images using compound image transforms, from which a small set of informative features with minimum redundancy are selected and fed into a Naïve Bayes Classifier (NBC).ResultsWe achieved an overall accuracy of 89.2% and 85.12% with a leave-one-out cross-validation and 10-fold cross-validation method, respectively. This study proposes a fully automated way for the classification of different ACG mechanisms, which is without intervention of doctors and less subjective when compared to the existing methods.ConclusionsWe directly extracted the compound image transformed features from the raw AS-OCT images without any segmentation and parameter measurement. Our method provides a completely automated and efficient way for the classification of different ACG mechanisms.