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Title: Bright Retinal Lesions Detection using Colour Fundus Images Containing Reflective Features

Abstract

In the last years the research community has developed many techniques to detect and diagnose diabetic retinopathy with retinal fundus images. This is a necessary step for the implementation of a large scale screening effort in rural areas where ophthalmologists are not available. In the United States of America, the incidence of diabetes is worryingly increasing among the young population. Retina fundus images of patients younger than 20 years old present a high amount of reflection due to the Nerve Fibre Layer (NFL), the younger the patient the more these reflections are visible. To our knowledge we are not aware of algorithms able to explicitly deal with this type of reflection artefact. This paper presents a technique to detect bright lesions also in patients with a high degree of reflective NFL. First, the candidate bright lesions are detected using image equalization and relatively simple histogram analysis. Then, a classifier is trained using texture descriptor (Multi-scale Local Binary Patterns) and other features in order to remove the false positives in the lesion detection. Finally, the area of the lesions is used to diagnose diabetic retinopathy. Our database consists of 33 images from a telemedicine network currently developed. When determining moderate tomore » high diabetic retinopathy using the bright lesions detected the algorithm achieves a sensitivity of 100% at a specificity of 100% using hold-one-out testing.« less

Authors:
 [1];  [1];  [1];  [1];  [1];  [2]
  1. ORNL
  2. University of Tennessee, Knoxville (UTK)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE; Work for Others (WFO)
OSTI Identifier:
1001713
DOE Contract Number:  
DE-AC05-00OR22725
Resource Type:
Conference
Resource Relation:
Conference: WORLD CONGRESS 2009 - MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING, Munich, Germany, 20090907, 20091207
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; RETINA; DIAGNOSTIC TECHNIQUES; IMAGES; DATA PROCESSING; DISEASES; REFLECTIVITY; ALGORITHMS; DETECTION

Citation Formats

Giancardo, Luca, Karnowski, Thomas Paul, Chaum, Edward, Meriaudeau, Fabrice, Tobin Jr, Kenneth William, and Li, Yaquin. Bright Retinal Lesions Detection using Colour Fundus Images Containing Reflective Features. United States: N. p., 2009. Web.
Giancardo, Luca, Karnowski, Thomas Paul, Chaum, Edward, Meriaudeau, Fabrice, Tobin Jr, Kenneth William, & Li, Yaquin. Bright Retinal Lesions Detection using Colour Fundus Images Containing Reflective Features. United States.
Giancardo, Luca, Karnowski, Thomas Paul, Chaum, Edward, Meriaudeau, Fabrice, Tobin Jr, Kenneth William, and Li, Yaquin. Thu . "Bright Retinal Lesions Detection using Colour Fundus Images Containing Reflective Features". United States.
@article{osti_1001713,
title = {Bright Retinal Lesions Detection using Colour Fundus Images Containing Reflective Features},
author = {Giancardo, Luca and Karnowski, Thomas Paul and Chaum, Edward and Meriaudeau, Fabrice and Tobin Jr, Kenneth William and Li, Yaquin},
abstractNote = {In the last years the research community has developed many techniques to detect and diagnose diabetic retinopathy with retinal fundus images. This is a necessary step for the implementation of a large scale screening effort in rural areas where ophthalmologists are not available. In the United States of America, the incidence of diabetes is worryingly increasing among the young population. Retina fundus images of patients younger than 20 years old present a high amount of reflection due to the Nerve Fibre Layer (NFL), the younger the patient the more these reflections are visible. To our knowledge we are not aware of algorithms able to explicitly deal with this type of reflection artefact. This paper presents a technique to detect bright lesions also in patients with a high degree of reflective NFL. First, the candidate bright lesions are detected using image equalization and relatively simple histogram analysis. Then, a classifier is trained using texture descriptor (Multi-scale Local Binary Patterns) and other features in order to remove the false positives in the lesion detection. Finally, the area of the lesions is used to diagnose diabetic retinopathy. Our database consists of 33 images from a telemedicine network currently developed. When determining moderate to high diabetic retinopathy using the bright lesions detected the algorithm achieves a sensitivity of 100% at a specificity of 100% using hold-one-out testing.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 2009},
month = {Thu Jan 01 00:00:00 EST 2009}
}

Conference:
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