Name
Abstract | ||
|---|---|---|
Blood vessel segmentation of retinal images plays an important role in the diagnosis of eye diseases. In this paper, we propose an automatic unsupervised blood vessel segmentation method for retinal images. Firstly, a multi-dimensional feature vector is constructed with the green channel intensity and the vessel enhanced intensity feature by the morphological operation. Secondly, self-organizing map (SOM) is exploited for pixel clustering, which is an unsupervised neural network. Finally, we classify each neuron in the output layer of SOM as retinal neuron or non-vessel neuron with Otsu’s method, and get the final segmentation result. Our proposed method is validated on the publicly available DRIVE database, and compared with the state-of-the-art algorithms. |
Year | Venue | Field |
|---|---|---|
2015 | ICIG | Computer vision,Feature vector,Pattern recognition,Segmentation,Computer science,Self-organizing map,Otsu's method,Artificial intelligence,Pixel,Retinal,Cluster analysis,Artificial neural network |
DocType | Citations | PageRank |
Conference | 1 | 0.37 |
References | Authors | |
8 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jingdan Zhang | 1 | 78 | 1.40 |
| Yingjie Cui | 2 | 1 | 1.39 |
| Wuhan Jiang | 3 | 75 | 0.99 |
| Le Wang | 4 | 75 | 0.99 |