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Title: Automatic 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. 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. Our algorithm is robust to segmentation uncertainties, does not need ground truth at lesion level, and is very fast, generating a diagnosis on an average of 4.4 seconds per image on an 2.6 GHz platform with an unoptimised Matlab implementation.

Authors:
 [1];  [1]; ORCiD logo [1];  [2];  [3]; ORCiD logo [1];  [2]
  1. ORNL
  2. University of Tennessee, Knoxville (UTK)
  3. University of North Carolina
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1038783
DOE Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article
Journal Name:
Medical Image Analysis
Additional Journal Information:
Journal Volume: N/A; Journal Issue: N/A
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; 60 APPLIED LIFE SCIENCES; ALGORITHMS; DETECTION; DIABETES MELLITUS; DIAGNOSIS; DIAGNOSTIC TECHNIQUES; EDEMA; EYES; IMAGES; MINORITY GROUPS; PATIENTS; RETINA; SENSE ORGANS DISEASES; TESTING; VISION

Citation Formats

Giancardo, Luca, Meriaudeau, Fabrice, Karnowski, Thomas, Li, Yi-Liang, Garg, Seema, Tobin Jr, Kenneth, and Chaum, Ed. Automatic Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets. United States: N. p., 2011. Web.
Giancardo, Luca, Meriaudeau, Fabrice, Karnowski, Thomas, Li, Yi-Liang, Garg, Seema, Tobin Jr, Kenneth, & Chaum, Ed. Automatic Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets. United States.
Giancardo, Luca, Meriaudeau, Fabrice, Karnowski, Thomas, Li, Yi-Liang, Garg, Seema, Tobin Jr, Kenneth, and Chaum, Ed. Thu . "Automatic Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets". United States.
@article{osti_1038783,
title = {Automatic Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets},
author = {Giancardo, Luca and Meriaudeau, Fabrice and Karnowski, Thomas and Li, Yi-Liang and Garg, Seema and Tobin Jr, Kenneth and Chaum, Ed},
abstractNote = {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. 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. Our algorithm is robust to segmentation uncertainties, does not need ground truth at lesion level, and is very fast, generating a diagnosis on an average of 4.4 seconds per image on an 2.6 GHz platform with an unoptimised Matlab implementation.},
doi = {},
journal = {Medical Image Analysis},
number = N/A,
volume = N/A,
place = {United States},
year = {2011},
month = {12}
}