Retinal Vessel Segmentation Method with Efficient Hybrid Features Fusion

被引：5
|
作者：
Cai Y. [1 ]
Gao X. [1 ]
Qiu C. [1 ]
Cui Y. [1 ]
机构：
[1] Faculty of Information Technology, Beijing University of Technology, Beijing
来源：
Cai, Yiheng (caiyiheng@bjut.edu.cn) | 1956年 / Science Press卷 / 39期
基金：
中国国家自然科学基金;
关键词：
Feature vetor; Machine learning; Retina; Support Vector Machine (SVM); Vessel segmentation;
D O I：
10.11999/JEIT161290
中图分类号：
学科分类号：
摘要：
How to apply machine learning to retinal vessel segmentation effectively has become a trend, however, choosing what kind of features for the blood vessels is still a problem. In this paper, the blood vessels of pixels are regarded as a theory of binary classification, and a hybrid 5D features for each pixel is put forward to extract retinal blood vessels from the background simplely and quickly. The 5D eigenvector includes Contrast Limited Adaptive Histgram Equalization (CLAHE), Gaussian matched filter, Hessian matrix transform, morphological bottom hat transform and Bar-selective Combination Of Shifted Filter Responses (B-COSFIRE). Then the fusion features are input into the Support Vector Machine (SVM) classifier to train a model that is needed. The proposed method is evaluated on two publicly available datasets of DRIVE and STARE, respectively. Se, Sp, Acc, Ppv, Npv, F1-measure are used to test the proposed method, and average classification accuracies are 0.9573 and 0.9575 on the DRIVE and STARE datasets, respectively. Performance results show that the fusion method also outperform the state-of-the-art method including B-COSFIRE and other currently proposed fusion features method. © 2017, Science Press. All right reserved.
引用
收藏
页码：1956 / 1963
页数：7
相关论文
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