Swamidoss Issac Niwas1, Weisi Lin2, Xiaolong Bai3, Chee Keong Kwoh4, C-C Jay Kuo5, Chelvin C Sng6, Maria Cecilia Aquino7, Paul T K Chew8. 1. School of Computer Engineering, Nanyang Technological University (NTU), 639798 Singapore, Singapore. Electronic address: issacniwas@ntu.edu.sg. 2. School of Computer Engineering, Nanyang Technological University (NTU), 639798 Singapore, Singapore. Electronic address: wslin@ntu.edu.sg. 3. The State Key Laboratory of Fluid Power Transmission and Control, Zhejiang University, Hangzhou 310027, People's Republic of China; School of Electrical and Electronics Engineering, Nanyang Technological University (NTU), 639798 Singapore, Singapore. Electronic address: mebaixl@gmail.com. 4. School of Computer Engineering, Nanyang Technological University (NTU), 639798 Singapore, Singapore. Electronic address: asckkwoh@ntu.edu.sg. 5. Ming Hsieh Department of Electrical Engineering, Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089, USA. Electronic address: cckuo@sipi.usc.edu. 6. Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228 Singapore, Singapore. Electronic address: chelvin@gmail.com. 7. Eye Surgery Centre, National University Health System (NUHS), 119228 Singapore, Singapore. Electronic address: mcdaquino@gmail.com. 8. Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore (NUS), 119228 Singapore, Singapore. Electronic address: ophchewp@nus.edu.sg.
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.
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.