Saffarzadeh Vahid Mohammadi, Osareh Alireza, Shadgar Bita
Department of Computer Engineering, Shahid Chamran University of Ahvaz, Khuzestan, Iran.
J Med Signals Sens. 2014 Apr;4(2):122-9.
Detecting blood vessels is a vital task in retinal image analysis. The task is more challenging with the presence of bright and dark lesions in retinal images. Here, a method is proposed to detect vessels in both normal and abnormal retinal fundus images based on their linear features. First, the negative impact of bright lesions is reduced by using K-means segmentation in a perceptive space. Then, a multi-scale line operator is utilized to detect vessels while ignoring some of the dark lesions, which have intensity structures different from the line-shaped vessels in the retina. The proposed algorithm is tested on two publicly available STARE and DRIVE databases. The performance of the method is measured by calculating the area under the receiver operating characteristic curve and the segmentation accuracy. The proposed method achieves 0.9483 and 0.9387 localization accuracy against STARE and DRIVE respectively.
检测血管是视网膜图像分析中的一项重要任务。在视网膜图像中存在明亮和黑暗病变的情况下，这项任务更具挑战性。在此，提出了一种基于线性特征来检测正常和异常眼底视网膜图像中血管的方法。首先，通过在感知空间中使用K均值分割来减少明亮病变的负面影响。然后，利用多尺度线算子来检测血管，同时忽略一些黑暗病变，这些黑暗病变具有与视网膜中线状血管不同的强度结构。所提出的算法在两个公开可用的STARE和DRIVE数据库上进行了测试。该方法的性能通过计算接收器操作特征曲线下的面积和分割精度来衡量。所提出的方法在STARE和DRIVE数据库上分别实现了0.9483和0.9387的定位精度。
