Automatic Diabetic Macular Edema Detection in Fundus Images Using Publicly Available Datasets

Journal Article · Thu Dec 01 04:00:00 EST 2011 · Medical Image Analysis

OSTI ID:1038783

Giancardo, Luca ^[1]; Meriaudeau, Fabrice ^[1]; Karnowski, Thomas ^[1]; Li, Yi-Liang ^[2]; Garg, Seema ^[3]; Tobin Jr, Kenneth ^[1]; Chaum, Ed ^[2]

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1. ORNL
2. University of Tennessee, Knoxville (UTK)
3. University of North Carolina

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.

• Research Organization: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE
• DOE Contract Number: AC05-00OR22725
• OSTI ID: 1038783
• Journal Information: Medical Image Analysis, Journal Name: Medical Image Analysis Journal Issue: N/A Vol. N/A
• Country of Publication: United States
• Language: English

Related Subjects

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