Blood Vessels Segmentation Using Independent Component Analysis
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Abstract
The blood vessel segmentation and exposure method perform a valuable clinical position in a computerized retinopathy research method. But, the expertise requires for manual segmentation of vessels. It is a long and time-taking task. Applying computational statistics for this determination would benefit in the practical retinal study. This retinal fundus image involves modifying low variations, which threaten the achievement of the segmentation method. A user-friendly GUI that is a MATLAB-based tool is used to segment the blood vessels using various services on the retinal image, including Grayscale conversion, Image Binarization, Gabor filtering, Independent component analysis. This paper has implemented an automatic retinal blood vessel segmentation method for the early diagnosis of diseases. The machine learning-based Independent Component Analysis (ICA) primarily employed for noise elimination and architecture, including varous stages such as image pre-processing, filtering, blood vessels segmentation, classification. We validated ICA architecture on fundus and retinal colour imaging. The empirical results show that the suggested model provides higher accuracy with 98.2% compared with previous algorithms.
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