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.
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