Name
Title | ||
|---|---|---|
Multi-Label Classification Scheme Based On Local Regression For Retinal Vessel Segmentation |
Abstract | ||
|---|---|---|
The segmentation of small blood vessels whose width is less than 2 pixels in retinal images is a challenging problem. Existed methods rarely focus on the differences between small vessels and big vessels when doing segmentation. Therefore, previous methods are not accurate enough on small blood vessel segmentation. To effectively segment small blood vessels in retinal images including big vessels, we proposed a novel multi-label classification scheme for retinal vessel segmentation. In our proposed scheme, a local de-regression model is designed for multi-labeling and a convolutional neural network is used for multi-label classification. At addition, a local regression method is utilized to transform multi-label into binary label for locating small vessels. The experimental results show that our method achieves prominent performance for automatic retinal vessel segmentation, especially for small blood vessels. |
Year | Venue | Keywords |
|---|---|---|
2018 | 2018 25TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP) | neural network, retinal vessel segmentation, regression, multi-label classification |
Field | DocType | ISSN |
Computer vision,Pattern recognition,Medical imaging,Convolutional neural network,Segmentation,Computer science,Local regression,Image segmentation,Multi-label classification,Pixel,Artificial intelligence,Retinal | Conference | 1522-4880 |
Citations | PageRank | References |
1 | 0.34 | 0 |
Authors | ||
6 | ||