Name
Abstract | ||
|---|---|---|
We present a supervised method for vessel segmentation in retinal images. The segmentation issue has been addressed as a pixel-level binary classification task, where the image is divided into patches and the classification (vessel or non-vessel) is performed on the central pixel of the patch. The input image is then segmented by classifying all of its pixels. A Convolutional Neural Network (CNN) has been used for the classification task, and the network has been trained on a large number of samples, in order to obtain an adequate generalization ability. Since blood vessels are characterized by a linear structure, we have introduced a further layer into the classic CNN including directional filters. The method has been tested on the DRIVE dataset producing satisfactory results, and its performance has been compared to that of other supervised and unsupervised methods. |
Year | Venue | Field |
|---|---|---|
2017 | CIARP | Computer vision,Vessel segmentation,Pattern recognition,Binary classification,Convolutional neural network,Segmentation,Computer science,Linear complex structure,Retinal image,Artificial intelligence,Pixel,Retinal |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
16 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nadia Brancati | 1 | 29 | 7.76 |
| Maria Frucci | 2 | 190 | 26.24 |
| Diego Gragnaniello | 3 | 162 | 12.51 |
| Daniel Riccio | 4 | 170 | 23.60 |