Retinal Vessel Segmentation Based on Adaptive Random Sampling
Nicolas Jouandeau 1, Zhi Yan 1, Patrick Greussay 1, Beiji Zou 2, and
Yao Xiang 2
1. Advanced Computing Laboratory of Saint-Denis (LIASD), Paris 8 University, 93526 Saint-Denis, France
2. School of Information Science and Engineering, Central South University, Changsha 410083, PR China
2. School of Information Science and Engineering, Central South University, Changsha 410083, PR China
Abstract—This paper presents a method for the extraction of blood vessels from fundus images. The proposed method is an unsupervised learning method which can automatically segment retinal blood vessels based on an adaptive random sampling algorithm. This algorithm consists in taking an adequate number of random samples in fundus images, and all the samples are contracted to the position of the blood vessels, then the retinal vessels will be revealed. The basic algorithm framework is presented in this paper and several preliminary experiments validate the feasibility and effectiveness of the proposed method.
Index Terms—Blood vessel segmentation, unsupervised learning, sampling algorithm, retinal vessels, fundus images
Cite: Nicolas Jouandeau, Zhi Yan, Patrick Greussay, and Beiji Zou, and Yao Xiang, "Retinal Vessel Segmentation Based on Adaptive Random Sampling," Journal of Medical and Bioengineering, Vol. 3, No. 3, pp. 199-202, September 2014. Doi: 10.12720/jomb.3.3.199-202
Index Terms—Blood vessel segmentation, unsupervised learning, sampling algorithm, retinal vessels, fundus images
Cite: Nicolas Jouandeau, Zhi Yan, Patrick Greussay, and Beiji Zou, and Yao Xiang, "Retinal Vessel Segmentation Based on Adaptive Random Sampling," Journal of Medical and Bioengineering, Vol. 3, No. 3, pp. 199-202, September 2014. Doi: 10.12720/jomb.3.3.199-202
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