AUTOMATIC RETINA EXUDATES SEGMENTATION WITHOUT A MANUALLY LABELLED TRAINING SET

Luca Giancardo (1) , Fabrice Meriaudeau (1) , T.P. Karnowski (2) , Li Yi (3) , Kenneth Tobin (2) , Edward Chaum (4)

1 Le2i - Laboratoire Electronique, Informatique et Image [UMR6306]
2 ORNL - Oak Ridge National Laboratory [Oak Ridge]
3 The University of Tennessee Health Science Center
4 The Neuroscience Institute

Diabetic macular edema (DME) is a common vision threatening complication of diabetic retinopathy. In a large scale screening environment DME can be assessed by detecting exudates (a type of bright lesions) in fundus images. In this work, two new methods for the detection of exudates are presented. The methods do not require a lesion training set so the need to ground-truth data is avoided with significant time savings and independence from human error. We evaluate our algorithm with a new publicly available dataset from various ethnic groups and levels of DME. Also, we compare our results with two recent exudate segmentation algorithms on the same dataset. In all of our tests, our algorithms are either outperforming or in line with existing methods. Additionally, the computational time is one order of magnitude less than similar methods.

Domaines: Traitement des images [eess.IV]

Luca Giancardo, Fabrice Meriaudeau, T.P. Karnowski, Li Yi, Kenneth Tobin, et al.. AUTOMATIC RETINA EXUDATES SEGMENTATION WITHOUT A MANUALLY LABELLED TRAINING SET. International Symposium on Biomedical Imaging, Mar 2011, Russia. pp.1. ⟨hal-00585177⟩