一种混合特征高效融合的视网膜血管分割方法

doi:10.11999/JEIT161290

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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.

Key words： Machine learning Retina Vessel segmentation Feature vetor Support Vector Machine (SVM)

Received: 28 November 2016 Published: 11 May 2017

PACS:

TP391

Fund:

The National Natural Science Foundation of China (61201360)

Corresponding Authors: CAI Yiheng E-mail: caiyiheng@bjut.edu.cn

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	Articles by authors
	CAI Yiheng
	GAO Xurong
	QIU Changyan
	CUI Yize

Cite this article:

CAI Yiheng,GAO Xurong,QIU Changyan等. Retinal Vessel Segmentation Method with Efficient Hybrid Features Fusion[J]. JEIT, 2017, 39(8): 1956-1963.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT161290 OR http://jeit.ie.ac.cn/EN/Y2017/V39/I8/1956

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