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.

© 1997 Optical Society of America

Discriminant analysis for recognition of human face images

Kamran Etemad and Rama Chellappa
J. Opt. Soc. Am. A 14(8) 1724-1733 (1997)

Hologram classification of occluded and deformable objects with speckle noise contamination by deep learning

H. H. S. Lam, P. W. M. Tsang, and T.-C. Poon
J. Opt. Soc. Am. A 39(3) 411-417 (2022)

Fractal character of the neural spike train in the visual system of the cat

Malvin C. Teich, Conor Heneghan, Steven B. Lowen, Tsuyoshi Ozaki, and Ehud Kaplan
J. Opt. Soc. Am. A 14(3) 529-546 (1997)

A New Classification System for Radiation Detectors*

R. Clark Jones
J. Opt. Soc. Am. 39(5) 327-343 (1949)

Neural dynamics of motion grouping: from aperture ambiguity to object speed and direction

Jonathan Chey, Stephen Grossberg, and Ennio Mingolla
J. Opt. Soc. Am. A 14(10) 2570-2594 (1997)