Retinal Vessel Segmentation Method with Efficient Hybrid Features Fusion

CAI Yiheng; GAO Xurong; QIU Changyan; CUI Yize

doi:10.11999/JEIT161290

Volume 39 Issue 8

Aug. 2017

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CAI Yiheng, GAO Xurong, QIU Changyan, CUI Yize. Retinal Vessel Segmentation Method with Efficient Hybrid Features Fusion[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1956-1963. doi: 10.11999/JEIT161290

Citation:

CAI Yiheng, GAO Xurong, QIU Changyan, CUI Yize. Retinal Vessel Segmentation Method with Efficient Hybrid Features Fusion[J]. Journal of Electronics & Information Technology, 2017, 39(8): 1956-1963. doi: 10.11999/JEIT161290

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Retinal Vessel Segmentation Method with Efficient Hybrid Features Fusion

doi: 10.11999/JEIT161290

Funds:

The National Natural Science Foundation of China (61201360)

Received Date: 2016-11-28
Rev Recd Date: 2017-04-14
Publish Date: 2017-08-19

Abstract

Abstract

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.
- Machine learning,
- Retina,
- Vessel segmentation,
- Feature vetor,
- Support Vector Machine (SVM)

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References(15)

References

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