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Automatic Analysis Of Color Fundus Photographs And Its Application To The Diagnosis Of Diabetic Retinopathy

T. Walter, J. Klein
Published 2005 · Computer Science

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Medical image processing is the meeting of two sciences that behave in completely different ways. While medicine is a science where experience plays a majors role and where the practical use is evident, image processing—as a derivative of applied mathematics—is a more theoretical discipline. Hence, the conditions of this meeting need to be analyzed sophisticatedly; not everything possible to implement is useful, and not everything useful is possible to implement.
This paper references
10.1109/TMI.1982.4307580
Overview of a Unified SNR Analysis of Medical Imaging Systems
R. F. Wagner (1982)
10.2307/2531038
Image Analysis and Mathematical Morphology
J. Serra (1983)
10.1016/0031-3203(88)90057-X
Zero-crossing interval correction in tracing eye-fundus blood vessels
S. Tamura (1988)
10.1016/0734-189X(88)90022-9
A survey of thresholding techniques
P.K Sahoo (1988)
10.1109/42.34715
Detection of blood vessels in retinal images using two-dimensional matched filters.
S. Chaudhuri (1989)
10.1016/S0161-6420(89)32925-3
Image analysis of fundus photographs. The detection and measurement of exudates associated with diabetic retinopathy.
N. P. Ward (1989)
10.1364/AO.28.001061
Spectral reflectance of the human ocular fundus.
F. Delori (1989)
10.1201/9781482277234
Mathematical Morphology in Image Processing
E. Dougherty (1992)
10.1007/978-3-662-03039-4_13
Morphological Area Openings and Closings for Grey-scale Images
L. Vincent (1994)
10.1017/CBO9780511812651
Pattern Recognition and Neural Networks
B. Ripley (1996)
10.1109/42.730405
Mapping the human retina
A. Pinz (1998)
10.1136/bjo.83.8.902
Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images
C. Sinthanayothin (1999)
10.1136/bjo.83.2.231
Histochemical localisation of mitochondrial enzyme activity in human optic nerve and retina
R. Andrews (1999)
10.1109/ICIAP.1999.797681
Automatic segmentation of microaneurysms in retinal angiograms of diabetic patients
A. Mendonça (1999)
10.1109/83.931095
Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation
F. Zana (2001)
Automatic Recognition of Exudative Maculopathy using Fuzzy C- Means Clustering and Neural Networks
A. Osareh (2001)
10.1007/3-540-45497-7_43
Segmentation of Color Fundus Images of the Human Retina: Detection of the Optic Disc and the Vascular Tree Using Morphological Techniques
T. Walter (2001)
10.1001/ARCHOPHT.119.4.509
Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts.
S. Lee (2001)
10.1088/0031-9155/47/16/303
Monte Carlo modelling of the spectral reflectance of the human eye.
S. Preece (2002)
Colour Morphology and Snakes for Optic Disc Localisation
A. Osareh (2002)
10.1109/TMI.2002.806290
A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina
T. Walter (2002)
10.1108/sr.2000.08720cae.001
Morphological Image Analysis: Principles and Applications
P. Soille (2003)
Application de la morphologie mathématique au diagnostic de la rétinopathie diabétique à partir d' images couleur
T. Walter (2003)
10.1007/BF00920219
Automated detection and quantification of retinal exudates
R. Phillips (2004)
10.1007/BF00166760
Automated detection and quantification of microaneurysms in fluorescein angiograms
Timothy Spencer (2004)



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10.1177/1460458220935369
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Monserrate Intriago-Pazmiño (2020)
10.1007/978-3-030-50516-5
Image Analysis and Recognition: 17th International Conference, ICIAR 2020, Póvoa de Varzim, Portugal, June 24–26, 2020, Proceedings, Part II
A. Campilho (2020)
10.1007/978-3-030-50516-5_27
Wavelet-Based Retinal Image Enhancement
Safinaz ElMahmoudy (2020)
Bioimage Informatics for Phenomics
T. Walter (2020)
10.1167/tvst.7.2.23
Combining ODR and Blood Vessel Tracking for Artery–Vein Classification and Analysis in Color Fundus Images
M. Alam (2018)
COMPUTER AIDED DIAGNOSIS OF GLAUCOMA DETECTION USING DIGITAL FUNDUS IMAGE
Ganeshbabu (2017)
10.1016/j.compbiomed.2017.09.012
No-reference quality index for color retinal images
Lamiaa Abdel-Hamid (2017)
OPTIC DISC BOUNDARY SEGMENTATION IN RETINAL IMAGE USING MORPHOLOGY, EDGE DETECTION AND CIRCULAR HOUGH TRANSFORM
Cenam Tangshu (2017)
10.17148/IARJSET/NCETETE.2017.44
To Find Location of Optic Disc in Digital Fundus Images
Miss. Tejaswini S. Mane (2017)
An Approach for the Detection of Proliferative Diabetic Retinopathy
Keith A. Goatman (2017)
10.1117/1.JBO.21.9.096007
Retinal image quality assessment based on image clarity and content
Lamiaa Abdel-Hamid (2016)
10.1088/2057-1976/aa7d16
Superimposition of eye fundus images for longitudinal analysis from large public health databases
Guillaume Noyel (2016)
10.29284/IJASIS.1.1.2015.1-11
COMPUTER AIDED DIAGNOSIS OF GLAUCOMA DETECTION USING DIGITAL FUNDUS IMAGE
R. GaneshbabuT (2015)
10.5507/bp.2015.053
Optic nerve head segmentation using fundus images and optical coherence tomography images for glaucoma detection.
T. R. Ganesh Babu (2015)
Identification of Diabetic Retinopathy in Fundus Images Using Segment Features and Morphological Features
G. Premanandan (2014)
10.13005/BPJ/544
Segmentation of Optic Nerve Head for Glaucoma Detection using Fundus images
Ganesh Babu.T.R (2014)
Automatic Diagnosis of Diabetic Retinopathy Using Fundus Images (Using Neural Networks and Fuzzy C)
Charu Sharma (2014)
Automatic Identification of Optic Disc in Retinal Fundus Images
Shuvayu Goswami (2014)
MSAC's advice does not necessarily reflect the views of all individuals who participated in the MSAC evaluation.
Ben Ellery (2014)
Diabetic retinopathy diagnosis through multi-agent approaches
C. Pereira (2014)
10.1109/JEC-ECC.2013.6766410
Preprocessing of color retinal fundus images
Fatma A. Hashim (2013)
10.3109/02713683.2013.779722
Multifractal Geometry in Analysis and Processing of Digital Retinal Photographs for Early Diagnosis of Human Diabetic Macular Edema
S. Talu (2013)
Glaucoma Detection From Fundus Image Using Opencv
K. Narasimhan (2012)
An Approach for the Detection of Proliferative Diabetic Retinopathy
J. Arputham (2012)
AN EFFICIENT AUTOMATED SYSTEM FOR GLAUCOMA DETECTION USING FUNDUS IMAGE
K. Narasimhan (2011)
10.1109/TMI.2010.2099236
Detection of New Vessels on the Optic Disc Using Retinal Photographs
K. Goatman (2011)
Fractal and multifractal analysis of human retinal vascular network: a review.
Ş. Ţălu (2011)
Colour retinal image segmentation for computer-aided fundus diagnosis
Jia Jane Yuo (2010)
10.1109/TMI.2010.2053042
Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection, and Feature Extraction Techniques
A. Aquino (2010)
Automated Optic Disc Detection in Retinal Images of Patients with Diabetic Retinopathy and Risk of Macular Edema
A. Aquino (2009)
10.1016/j.media.2007.05.001
Automatic detection of microaneurysms in color fundus images
T. Walter (2007)
Automated Analysis of Longitudinal Changes in Color Retinal Fundus Images for Monitoring Diabetic Retinopathy
H. Narasimha-Iyer (2004)
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