Abstract
We have developed an autonomous objective classification scheme for degree of nuclear opacification. The algorithm was developed by using a series of color 35-mm slides acquired with a Topcon photo slit-lamp microscope and use of standard camera settings. The photographs were digitized, and first, and second-order gray-level statistics were extracted from within circular regions of the nucleus. Classifications of severity were performed by using these features as input to a neural network. Training versus classification performance was tested by using photographs of different eyes, and test/retest classification reproducibility was evaluated by using paired photographs of the same eyes. We demonstrate good performance of the classifier against subjective assessments rendered by the Wilmer grading system [Invest. Ophthalmol. Visual Sci. 29, 73 (1988)] and markedly better test/retest reproducibility.
Original language | English (US) |
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Pages (from-to) | 1197-1204 |
Number of pages | 8 |
Journal | Journal of the Optical Society of America A: Optics and Image Science, and Vision |
Volume | 14 |
Issue number | 6 |
DOIs | |
State | Published - Jun 1997 |
Keywords
- Cataract
- Classification
- Nuclear
- Opacification
- Standards
ASJC Scopus subject areas
- Electronic, Optical and Magnetic Materials
- Atomic and Molecular Physics, and Optics
- Computer Vision and Pattern Recognition