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- W2785718443 abstract "In ophthalmology, human eye is an important diagnostic factor for studying vascular network. Segmentation of fundus image is important due to varying size of blood vessels, contrast and presence of different pathologies such as microaneurysms and hemorrhages. This work proposes a new method to find the exudates (patches) from the blood vessels of an eye during diabetic retinopathy treatment. For analysis, input retinal image is separated into three planes i.e., red, green and blue, from which green channels are selected, which are further processed using Daubechies wavelet transform to estimate the grey level co-occurrence matrix (GLCM) features. These features are processed using probabilistic neural network (PNN) and input retinal image is compared with the database image to classify it as normal or abnormal. Morphological operations are applied to the abnormal image to extract the blood vessels and then fuzzy c-means clustering is applied in the extracted blood vessels to detect the exudates. The accuracy and sensitivity of the result obtained by the proposed method is comparatively better and are 0.9776 and 0.9677 respectively. The method can be applied successfully and the blood vessels and exudates can be effectively detected." @default.
- W2785718443 created "2018-02-23" @default.
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- W2785718443 date "2017-04-01" @default.
- W2785718443 modified "2023-09-26" @default.
- W2785718443 title "Retinal blood vessels extraction and detection of exudates using wavelet transform and pnn approach for the assessment of diabetic retinopathy" @default.
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- W2785718443 doi "https://doi.org/10.1109/iccsp.2017.8286743" @default.
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