DIWU Peng-peng, HU Ya-qi
（School of Electronic and Information Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China）
Abstract： This paper presents a supervised learning algorithm for retinal vascular segmentation based on classification and regression tree (CART) algorithm and improved adptive bosting (AdaBoost). Local binary patterns (LBP) texture features and local features are extracted by extracting, reversing, dilating and enhancing the green components of retinal images to construct a 17-dimensional feature vector. A dataset is constructed by using the feature vector and the data manually marked by the experts. The feature is used to generate CART binary tree for nodes, where CART binary tree is as the AdaBoost weak classifier, and AdaBoost is improved by adding some re-judgment functions to form a strong classifier. The proposed algorithm is simulated on the digital retinal images for vessel extraction (DRIVE). The experimental results show that the proposed algorithm has higher segmentation accuracy for blood vessels, and the result basically contains complete blood vessel details. Moreover, the segmented blood vessel tree has good connectivity, which basically reflects the distribution trend of blood vessels. Compared with the traditional AdaBoost classification algorithm and the support vector machine (SVM) based classification algorithm, the proposed algorithm has higher average accuracy and reliability index, which is similar to the segmentation results of the state-of-the-art segmentation algorithm.
Key words： classification and regression tree (CART); improved adptive boosting (AdaBoost); retinal blood vessel; local binary pattern (LBP) texture
CLD number： TP391.41 Document code： A
Article ID： 1674-8042（2019）01-0061-08 doi： 10.3969/j.issn.1674-8042.2019.01.009
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基于CART决策树与改进的AdaBoost的视网膜血管提取算法
第五朋朋， 胡亚琦
（兰州交通大学 电子与信息工程学院， 甘肃 兰州 730070）
摘 要: 本文提出了一种分类回归树（Classification and regression tree， CART）算法与改进的AdaBoost相结合的视网膜血管分割的监督学习算法。 该算法对视网膜图像的绿色分量提取、反转、膨胀和增强后分别提取LBP（local binary patterns）纹理特征和局部特征， 从而构建出17维特征向量。 利用特征向量与专家手工标注的数据构造一个数据集， 以特征为节点生成CART二叉树， 将CART二叉树作为AdaBoost的弱分类器， 通过加入再判决函数对AdaBoost做出一定改进， 从而形成强分类器。 本算法在DRIVE（digital retinal images for vessel extraction）数据库上进行了实验仿真， 实验结果表明， 本文所提出的改进算法对血管的分割精度高， 包含了完整的血管细节， 而且分割出来的血管树的连通性较好， 能够反映出血管的分布走势。 与传统的AdaBoost分类算法和基于SVM（support vector machine）的分类算法相比， 本文所提出的改进算法的平均准确率和可靠性指标都比较高。
关键词: 分类回归数； 改进的AdaBoost； 视网膜血管； 局部二进模式纹理
引用格式： DIWU Peng-peng, HU Ya-qi. A retinal blood vessel extraction algorithm based on CART decision tree and improved AdaBoost. Journal of Measurement Science and Instrumentation, 2019, 10(1)： 61-68. ［doi： 10.3969／j.issn.1674-8042.2019.01.009］
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