Automated anterior segment OCT image analysis for Angle Closure Glaucoma mechanisms classification. Issue 130 (July 2016)

• Record Type: Journal Article
• Authors: Niwas, Swamidoss Issac; Lin, Weisi; Bai, Xiaolong; Kwoh, Chee Keong; Jay Kuo, C.-C.; Sng, Chelvin C.; Aquino, Maria Cecilia; Chew, Paul T.K.
• Is Part Of: Computer methods and programs in biomedicine. Issue 130(2016)
• Page Start: 65
• Page End: 75
• Publication Date: 2016-07
• Subjects: Angle closure glaucoma -- Compound image transforms -- Feature selection -- Segmentation-free method -- Machine learning classifier; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2016.03.018
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: 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. Abstract: Background and objectives: Angle 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. Methods: In 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). Results: We 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. Conclusions: We 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.