NDLI: Automated feature extraction for early detection of diabetic retinopathy in fundus images

Content Provider	IEEE Xplore Digital Library
Author	Ravishankar, S. Jain, A. Mittal, A.
Copyright Year	2009
Description	Author affiliation: Univ. of Illinois at Urbana-Champaign, Urbana, IL, USA (Ravishankar, S.) \|\| Univ. of Maryland, College Park, MD, USA (Jain, A.) \|\| Indian Inst. of Technol. Madras, Chennai, India (Mittal, A.)
Abstract	Automated detection of lesions in retinal images can assist in early diagnosis and screening of a common disease: Diabetic Retinopathy. A robust and computationally efficient approach for the localization of the different features and lesions in a fundus retinal image is presented in this paper. Since many features have common intensity properties, geometric features and correlations are used to distinguish between them. We propose a new constraint for optic disk detection where we first detect the major blood vessels and use the intersection of these to find the approximate location of the optic disk. This is further localized using color properties. We also show that many of the features such as the blood vessels, exudates and microaneurysms and hemorrhages can be detected quite accurately using different morphological operations applied appropriately. Extensive evaluation of the algorithm on a database of 516 images with varied contrast, illumination and disease stages yields 97.1% success rate for optic disk localization, a sensitivity and specificity of 95.7%and 94.2%respectively for exudate detection and 95.1% and 90.5% for microaneurysm/hemorrhage detection. These compare very favorably with existing systems and promise real deployment of these systems.
Starting Page	210
Ending Page	217
File Size	1022081
Page Count	8
File Format	PDF
ISBN	9781424439928
ISSN	10636919
DOI	10.1109/CVPR.2009.5206763
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Feature extraction Diabetes Retinopathy Optical sensors Lesions Retina Diseases Geometrical optics Blood vessels Biomedical imaging
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Automated red lesion detection in diabetic retinopathy

A survey on automated microaneurysm detection in Diabetic Retinopathy retinal images

Feature Extraction for Early Detection of Diabetic Retinopathy

A model-based approach for automated feature extraction in fundus images

Detection of Diabetic Retinopathy in Fundus Images using Vector Quantization Technique

Automated identification of diabetic retinopathy stages using support vector machine

Better detection of retinal abnormalities by accurate detection of blood vessels in retina

Identification of different stages of Diabetic Retinopathy using artificial neural network

Automatic hemorrhages detection based on fundus images

Automated feature extraction for early detection of diabetic retinopathy in fundus images

Similar Documents

Automated red lesion detection in diabetic retinopathy

A survey on automated microaneurysm detection in Diabetic Retinopathy retinal images

Feature Extraction for Early Detection of Diabetic Retinopathy

A model-based approach for automated feature extraction in fundus images

Detection of Diabetic Retinopathy in Fundus Images using Vector Quantization Technique

Automated identification of diabetic retinopathy stages using support vector machine

Better detection of retinal abnormalities by accurate detection of blood vessels in retina

Identification of different stages of Diabetic Retinopathy using artificial neural network

Automatic hemorrhages detection based on fundus images

Automated feature extraction for early detection of diabetic retinopathy in fundus images