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 »
- Authors:
-
- ORNL
- 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. 2009.
"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 = {},
url = {https://www.osti.gov/biblio/1001713},
journal = {},
number = ,
volume = ,
place = {United States},
year = {Thu Jan 01 00:00:00 EST 2009},
month = {Thu Jan 01 00:00:00 EST 2009}
}