Automatic retina exudates segmentation without a manually labelled training set

Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy which can be assessed by detecting exudates (a type of bright lesion) in fundus images. In this work, two new methods for the detection of exudates are presented which do not use a supervised learning st...

Full description

Saved in:
Bibliographic Details
Main Authors: Giancardo, L, Meriaudeau, F, Karnowski, T P, Li, Y, Tobin, K W, Chaum, E
Format: Conference Proceeding
Language:English
Subjects:
Biomedical imaging
computer-aided diagnosis
fundus image database
Gold
Radiography
retina normalisation
segmentation
Variable speed drives
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy which can be assessed by detecting exudates (a type of bright lesion) in fundus images. In this work, two new methods for the detection of exudates are presented which do not use a supervised learning step; therefore, they do not require labelled lesion training sets which are time consuming to create, difficult to obtain and prone to human error. We introduce a new dataset of fundus images from various ethnic groups and levels of DME which we have made publicly available. We evaluate our algorithm with this dataset and compare our results with two recent exudate segmentation algorithms. In all of our tests, our algorithms perform better or comparable with an order of magnitude reduction in computational time.
ISSN:1945-7928
1945-8452
DOI:10.1109/ISBI.2011.5872661