Abstract
Image segmentation helps to analyze images by simplifying the representation of image. It is clear that there is no universal algorithm for image segmentation methods; different methods should be used depending on the application. In this paper multiscale blood vessel segmentation in retinal fundus images algorithm [1] was implemented and its parts were analyzed. In order to reduce noise, OpenCV blurring functions were used. Moreover, the problem of segmentation was described. It was observed that the blood vessel can be identified using the multiscale blood vessel segmentation in retinal fundus images algorithm. It also found that the preprocessing of the captured fundus images is very essential. Thus the results can be further enhanced by using selective and regional image smoothing functions according to the fundus images characteristics before applying the multiscale blood vessel segmentation in retinal fundus images algorithm.
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Sarbasova, A., Hasan, M.M. (2015). Multiscale Blood Vessel Segmentation in Retinal Fundus Images Algorithm Implementation and Analysis. In: Horne, R. (eds) Embracing Global Computing in Emerging Economies. EGC 2015. Communications in Computer and Information Science, vol 514. Springer, Cham. https://doi.org/10.1007/978-3-319-25043-4_11
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DOI: https://doi.org/10.1007/978-3-319-25043-4_11
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