New objective classification system for nuclear opacification.
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.
Duncan
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Johns Hopkins University Applied Physics Laboratory, Laurel, Maryland 20723-6099, USA. donald.duncan@jhuapl.edu
Shukla
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West
S K
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Schein
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D000465:Algorithms; D002386:Cataract / Q000145:classification* / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008954:Models, Biological; D010781:Photography; D015203:Reproducibility of Results; D012720:Severity of Illness Index