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Retinal blood vessel segmentation based on fractal dimension in spatial-frequency domain

机译:基于分形维数的频域视网膜血管分割

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Vessel segmentation is very important in an automatic screening system for fundus images. Vessels are often segmented and removed from retinal images before the other residual lesions are detected. Incomplete vessel removal usually causes a false positive in lesion detection, especially for Microaneurysms detection. Segmenting vessels in spatial image domain makes miss detection due to non illumination and noises in retinal images. Non-illumination problem can be disregarded by segmenting the vessels in spatial-frequency domain, which invariant subbands can be ignored. This paper presents a new retinal blood vessel segmentation method based on fractal dimension in spatial-frequency domain of retinal images. The fractal dimension value of each pixel is computed in order to extract the vessels from their retinal background. The performance of the proposed method is evaluated and compared with the experts' diagnosis in the STARE database.
机译:在眼底图像自动筛选系统中,血管分割非常重要。在检测到其他残留病变之前,通常将血管分割并从视网膜图像中去除。不完全清除血管通常会在病变检测中导致假阳性,尤其是对于微动脉瘤检测而言。由于非照明和视网膜图像中的噪声,在空间图像域中对血管进行分割可进行漏检。通过在空间频域中分割血管可以忽略非照明问题,可以忽略不变的子带。提出了一种基于分形维数的视网膜图像空间频域视网膜血管分割方法。计算每个像素的分形维数,以便从其视网膜背景中提取血管。评估了所提出方法的性能,并与STARE数据库中的专家诊断进行了比较。

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