6533b86efe1ef96bd12cac4f
RESEARCH PRODUCT
AUTOMATIC RETINA EXUDATES SEGMENTATION WITHOUT A MANUALLY LABELLED TRAINING SET
Fabrice MeriaudeauKenneth W. TobinY. LiLuca GiancardoThomas P. KarnowskiEdward Chaumsubject
genetic structures02 engineering and technologyFundus (eye)030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicine[ INFO.INFO-TI ] Computer Science [cs]/Image Processing0202 electrical engineering electronic engineering information engineeringmedicineMedical imagingSegmentationComputer visionRetinabusiness.industrySupervised learningDiabetic retinopathyImage segmentationmedicine.diseaseeye diseasesmedicine.anatomical_structure[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV]Computer-aided diagnosis[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]020201 artificial intelligence & image processingArtificial intelligencebusinessdescription
International audience; 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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2011-03-01 |