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• W2286502495 abstract "Purpose: To develop a fast algorithm for automatic accurate extraction of blood vessels from retinal images in order to facilitate image analysis and minimize the rate of diagnosis error. Methods: We proposed an efficient algorithm using adaptive filter and thresholding technique which are based on fuzzy entropy. The data included retinal images from 60 index normals and patients (including 38 men and 22 women) obtained from STARE and DRIVE databases. The mean subject age was 47.6±9.1 years. In images from index patients, the presence of abnormal tissue was confirmed by two specialists. Initially, image blurring caused by vibrations during image capturing was eliminated using designed Wiener filter. The impact of optical reflection in each image was minimized through transferring images into the HSV space and selecting the spatial mask for retina. Extraction of retinal blood vessels was done using adaptive filtering. Finally, to separate the main artery of the retina from the rest of the tissues, the optimal threshold was obtained which was defined by the fuzzy entropy technique. Results: By using combined methods in pre-processing step of retinal images, the optic disc border and radiant reflections of ophthalmoscope disappeared in the output image. Therefore, the accuracy (0.9475±0.08) and F-measure (0.7426±0.05) increased significantly. Conclusion: Fast and accurate extraction of blood vessels in the retinal images enables the ophthalmologist to predict disease progression, hence suggesting appropriate treatment modality." @default.
• W2286502495 created "2016-06-24" @default.
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• W2286502495 date "2014-01-01" @default.
• W2286502495 modified "2023-09-24" @default.
• W2286502495 title "Designing an Accurate and Fast Algorithm to Extract the Retinal Vessels Using Adaptive Filters and Thresholding Based on Entropy Fuzzy" @default.
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