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PARALLEL NEAREST NEIGHBOUR CLUSTERING ALGORITHM (PNNCA) FOR SEGMENTING RETINAL BLOOD VESSELS

机译:用于视网膜血管细分的并行近邻近邻聚类算法(PNNCA)

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In this paper, the design and implementation of a recently developed clustering algorithm NNCA [1], Nearest Neighbour Clustering Algorithm, is proposed in conjunction with a Fast K Nearest Neighbour (FKNN) strategy for further reduction in processing time. The parallel algorithm (PNNCA) has the ability to cluster pixels of retinal images into those belonging to blood vessels and others not belonging to blood vessels in a reasonable time.
机译:在本文中,结合最近的快速K最近邻居(FKNN)策略,提出了最近开发的聚类算法NNCA [1](最近邻居聚类算法)的设计和实现,以进一步减少处理时间。并行算法(PNNCA)能够在合理的时间内将视网膜图像的像素聚类到属于血管的像素和不属于血管的像素。

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