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- W2805484247 abstract "Accurate blood vessel detection is of significant clinical importance for detection of diseases like hypertension, diabetic retinopathy, stroke, and arteriosclerosis. This paper demonstrates a novel unsupervised technique to address the problem of retinal blood vessel detection. In this method, vessel enhancement is done using an adaptive manifold filter and the background estimation by a large median filter. Mathematical morphology and iterative thresholding operations are done to segment the blood vessels. Extensive experiment has been carried out on two publicly available datasets, i.e., DRIVE and STARE that show promising results with accuracy 0.9513 and 0.9553, respectively. These results show better performance than state-of-the-art methods, both in terms of quantitative measures and visually. The encouraging performance and effectiveness of the proposed method make it suitable for computer-aided diagnosis of retinal images." @default.
- W2805484247 created "2018-06-13" @default.
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- W2805484247 date "2018-01-01" @default.
- W2805484247 modified "2023-09-22" @default.
- W2805484247 title "A Novel Unsupervised Framework for Retinal Vasculature Segmentation" @default.
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- W2805484247 doi "https://doi.org/10.1007/978-981-10-8240-5_55" @default.
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