Loading…
Automatic detection of neovascularization in retinal images using extreme learning machine
Diabetic Retinopathy is one complication of diabetes, which can cause blindness. Diabetic retinopathy can be divided into Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopahy (PDR), and neovascularization is a key symbol to make diagnosis between them. An automatic det...
Saved in:
Published in: | Neurocomputing (Amsterdam) 2018-02, Vol.277, p.218-227 |
---|---|
Main Authors: | , , , , |
Format: | Article |
Language: | English |
Subjects: | |
Citations: | Items that this one cites Items that cite this one |
Online Access: | Get full text |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | Diabetic Retinopathy is one complication of diabetes, which can cause blindness. Diabetic retinopathy can be divided into Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopahy (PDR), and neovascularization is a key symbol to make diagnosis between them. An automatic detection of neovascularization in retinal images using extreme learning machine is proposed. Furthermore, we use a series of filter banks to get the features of neovascularization from retinal images. The detection framework is evaluated with images annotated by expert ophthalmologists based on the images selected from several public retinal image databases. The experimental results illustrate that the framework can mark and show the suspected neovascularization regions to ophthalmologists, and thus support for their decision making. |
---|---|
ISSN: | 0925-2312 1872-8286 |
DOI: | 10.1016/j.neucom.2017.03.093 |