An improved blood vessel segmentation algorithm on the basis of traditional Frangi filtering and the mathematical morphological method was proposed to solve the low accuracy of automatic blood vessel segmentation of fundus retinal images and high complexity of algorithms. First, a global enhanced image was generated by using the contrast-limited adaptive histogram equalization algorithm of the retinal image. An improved Frangi Hessian model was constructed by introducing the scale equivalence factor and eigenvector direction angle of the Hessian matrix into the traditional Frangi filtering algorithm to enhance blood vessels of the global enhanced image. Next, noise interferences surrounding small blood vessels were eliminated through the improved mathematical morphological method. Then, blood vessels were segmented using the Otsu threshold method. The improved algorithm was tested by the public DRIVE and STARE data sets. According to the test results, the average segmentation accuracy, sensitivity, and specificity of retinal images in DRIVE and STARE are 95.54%, 69.42%, and 98.02% and 94.92%, 70.19%, and 97.71%, respectively. The improved algorithm achieved high average segmentation accuracy and low complexity while promising segmentation sensitivity. This improved algorithm can segment retinal vessels more accurately than other algorithms.

中文翻译：

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基于改进的 Frangi 和数学形态学的眼底视网膜图像血管分割

针对眼底视网膜图像血管自动分割精度低、算法复杂度高的问题，在传统Frangi滤波的基础上，结合数学形态学方法，提出了一种改进的血管分割算法。首先，通过使用视网膜图像的对比度受限的自适应直方图均衡算法生成全局增强图像。通过将Hessian矩阵的尺度等价因子和特征向量方向角引入到传统的Frangi滤波算法中，构建了改进的Frangi Hessian模型，对全局增强图像的血管进行增强。接下来，通过改进的数学形态学方法消除了小血管周围的噪声干扰。然后，使用大津阈值法分割血管。改进后的算法通过公开的 DRIVE 和 STARE 数据集进行了测试。根据测试结果，DRIVE和STARE中视网膜图像的平均分割精度、灵敏度和特异性分别为95.54%、69.42%和98.02%和94.92%、70.19%和97.71%。改进后的算法在保证分割灵敏度的同时，实现了高平均分割精度和低复杂度。这种改进的算法可以比其他算法更准确地分割视网膜血管。改进后的算法在保证分割灵敏度的同时，实现了高平均分割精度和低复杂度。这种改进的算法可以比其他算法更准确地分割视网膜血管。改进后的算法在保证分割灵敏度的同时，实现了高平均分割精度和低复杂度。这种改进的算法可以比其他算法更准确地分割视网膜血管。