Computer Aided Diagnostic of Dry Eye: Toward an Automatic Diagnosis from Noisy Tear Ferning Images

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This work focuses on the use of digital image analysis for automatic diagnose of Dry Eye. Recently, scaling of tear ferning images been used to diagnose dry eye. A classification approach in wavelet space is proposed as an initial step toward an automatic diagnose of dry eye. Correlation coefficient, in wavelet space, between a reference image and unclassified noisy tear ferning image, at different level of wavelet decomposition is used as classifier. The noise in the images has a great impact on the value of the correlation coefficient (CF). The noise reduction using wavelet technics at different level of wavelet decomposition (WD) provides a strong improvement for the classification (diagnose) of tear ferning noisy images. A set of reference image representing the scaling of the tear ferning images is used.

Read more at  https://www.scitechnol.com/peer-review/computer-aided-diagnostic-of-dry-eye-toward-an-automatic-diagnosis-from-noisy-tear-ferning-images-GWbl.php?article_id=5809

Visit Journal at https://www.scitechnol.com/diagnostic-techniques-biomedical-analysis.php

Submit manuscript at editor.jdtba@scitechnol.com

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