Anterior Chamber Angle Shape Analysis and Classification of Glaucoma in SS-OCT Images
Optical coherence tomography is a high resolution, rapid, and noninvasive diagnostic tool for angle closure glaucoma. In this paper, we present a new strategy for the classification of the angle closure glaucoma using morphological shape analysis of the iridocorneal angle. The angle structure config...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Journal of Ophthalmology |
| Online Access: | http://dx.doi.org/10.1155/2014/942367 |
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| author | Soe Ni Ni J. Tian Pina Marziliano Hong-Tym Wong |
| author_facet | Soe Ni Ni J. Tian Pina Marziliano Hong-Tym Wong |
| author_sort | Soe Ni Ni |
| collection | DOAJ |
| description | Optical coherence tomography is a high resolution, rapid, and noninvasive diagnostic tool for angle closure glaucoma. In this paper, we present a new strategy for the classification of the angle closure glaucoma using morphological shape analysis of the iridocorneal angle. The angle structure configuration is quantified by the following six features: (1) mean of the continuous measurement of the angle opening distance; (2) area of the trapezoidal profile of the iridocorneal angle centered at Schwalbe's line; (3) mean of the iris curvature from the extracted iris image; (4) complex shape descriptor, fractal dimension, to quantify the complexity, or changes of iridocorneal angle; (5) ellipticity moment shape descriptor; and (6) triangularity moment shape descriptor. Then, the fuzzy k nearest neighbor (fkNN) classifier is utilized for classification of angle closure glaucoma. Two hundred and sixty-four swept source optical coherence tomography (SS-OCT) images from 148 patients were analyzed in this study. From the experimental results, the fkNN reveals the best classification accuracy (99.11±0.76%) and AUC (0.98±0.012) with the combination of fractal dimension and biometric parameters. It showed that the proposed approach has promising potential to become a computer aided diagnostic tool for angle closure glaucoma (ACG) disease. |
| format | Article |
| id | doaj-art-ef7a039706354842a6a38cb4b266cf3e |
| institution | Kabale University |
| issn | 2090-004X 2090-0058 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Ophthalmology |
| spelling | doaj-art-ef7a039706354842a6a38cb4b266cf3e2025-02-03T06:13:40ZengWileyJournal of Ophthalmology2090-004X2090-00582014-01-01201410.1155/2014/942367942367Anterior Chamber Angle Shape Analysis and Classification of Glaucoma in SS-OCT ImagesSoe Ni Ni0J. Tian1Pina Marziliano2Hong-Tym Wong3School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, SingaporeSchool of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, SingaporeSchool of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, SingaporeDepartment of Ophthalmology, Tan Tock Seng Hospital, 11 Jalan Tan Tock Seng, 308433, SingaporeOptical coherence tomography is a high resolution, rapid, and noninvasive diagnostic tool for angle closure glaucoma. In this paper, we present a new strategy for the classification of the angle closure glaucoma using morphological shape analysis of the iridocorneal angle. The angle structure configuration is quantified by the following six features: (1) mean of the continuous measurement of the angle opening distance; (2) area of the trapezoidal profile of the iridocorneal angle centered at Schwalbe's line; (3) mean of the iris curvature from the extracted iris image; (4) complex shape descriptor, fractal dimension, to quantify the complexity, or changes of iridocorneal angle; (5) ellipticity moment shape descriptor; and (6) triangularity moment shape descriptor. Then, the fuzzy k nearest neighbor (fkNN) classifier is utilized for classification of angle closure glaucoma. Two hundred and sixty-four swept source optical coherence tomography (SS-OCT) images from 148 patients were analyzed in this study. From the experimental results, the fkNN reveals the best classification accuracy (99.11±0.76%) and AUC (0.98±0.012) with the combination of fractal dimension and biometric parameters. It showed that the proposed approach has promising potential to become a computer aided diagnostic tool for angle closure glaucoma (ACG) disease.http://dx.doi.org/10.1155/2014/942367 |
| spellingShingle | Soe Ni Ni J. Tian Pina Marziliano Hong-Tym Wong Anterior Chamber Angle Shape Analysis and Classification of Glaucoma in SS-OCT Images Journal of Ophthalmology |
| title | Anterior Chamber Angle Shape Analysis and Classification of Glaucoma in SS-OCT Images |
| title_full | Anterior Chamber Angle Shape Analysis and Classification of Glaucoma in SS-OCT Images |
| title_fullStr | Anterior Chamber Angle Shape Analysis and Classification of Glaucoma in SS-OCT Images |
| title_full_unstemmed | Anterior Chamber Angle Shape Analysis and Classification of Glaucoma in SS-OCT Images |
| title_short | Anterior Chamber Angle Shape Analysis and Classification of Glaucoma in SS-OCT Images |
| title_sort | anterior chamber angle shape analysis and classification of glaucoma in ss oct images |
| url | http://dx.doi.org/10.1155/2014/942367 |
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