title: Exudate-based diabetic macular edema detection in fundus images using publicly available datasets.
abstract: 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, we introduce a new methodology for diagnosis of DME using a novel set of features based on colour, wavelet decomposition and automatic lesion segmentation. These features are employed to train a classifier able to automatically diagnose DME through the presence of exudation. We present a new publicly available dataset with ground-truth data containing 169 patients from various ethnic groups and levels of DME. This and other two publicly available datasets are employed to evaluate our algorithm. We are able to achieve diagnosis performance comparable to retina experts on the MESSIDOR (an independently labelled dataset with 1200 images) with cross-dataset testing (e.g., the classifier was trained on an independent dataset and tested on MESSIDOR). Our algorithm obtained an AUC between 0.88 and 0.94 depending on the dataset/features used. Additionally, it does not need ground truth at lesion level to reject false positives and is computationally efficient, as it generates a diagnosis on an average of 4.4s (9.3s, considering the optic nerve localisation) per image on an 2.6 GHz platform with an unoptimised Matlab implementation.
authors.affiliation: Oak Ridge National Laboratory/University of Burgundy Imaging, Signals, and Machine Learning (ISML), PO Box 2008, MS6075 Oak Ridge, TN 37831-6075, United States. giancardol@ornl.gov
authors.forename: Luca
authors.initials: L
authors.lastname: Giancardo
authors.forename: Fabrice
authors.initials: F
authors.lastname: Meriaudeau
authors.forename: Thomas P
authors.initials: TP
authors.lastname: Karnowski
authors.forename: Yaqin
authors.initials: Y
authors.lastname: Li
authors.forename: Seema
authors.initials: S
authors.lastname: Garg
authors.forename: Kenneth W
authors.initials: KW
authors.lastname: Tobin
authors.forename: Edward
authors.initials: E
authors.lastname: Chaum
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D003930:Diabetic Retinopathy / Q000473:pathology*; D005122:Exudates and Transudates / Q000166:cytology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008269:Macular Edema / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D042262:Retinoscopy / Q000379:methods*; D012680:Sensitivity and Specificity