Open AccessAn Ensemble Retinal Vessel Segmentation Based on Supervised Learning in Fundus Images
Author(s) -
Zhu Chengzhang,
Zou Beiji,
Xiang Yao,
Cui Jinkai,
Wu Hui
Publication year - 2016
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2016.05.016
Subject(s) - artificial intelligence , computer science , pattern recognition (psychology) , segmentation , computer vision , robustness (evolution) , support vector machine , feature vector , feature extraction , biochemistry , chemistry , gene
An ensemble method based on supervised learning for segmenting the retinal vessels in color fundus images is proposed on the basis of previous work of Zhu et al . For each pixel, a 36 dimensional feature vector is extracted, including local features, morphological transformation with multi‐scale and multi‐orientation, and divergence of vector field which is firstly used to extract feature of retinal image pixels. Then the feature vector is used as input data set to train the weak classifiers by the Classification and regression tree (CART). Finally, an AdaBoost classifier is constructed by iteratively training for the retinal vessels segmentation. The experimental results on the public Digital retinal images for vessel extraction (DRIVE) database demonstrate that the proposed method is efficient and robust on the fundus images with lesions when compared with the other methods. Meanwhile, the proposed method also exhibits high robustness on a new Retinal images for screening (RIS) database. The average accuracy, sensitivity, and specificity of improved method are 0.9535, 0.8319 and 0.9607, respectively. It has potential applications for computer‐aided diagnosis and disease screening.