Walter, T.,
Klein, J.,
Massin, P.,
Erginay, A.,
A contribution of image processing to the diagnosis of diabetic
retinopathy-detection of exudates in color fundus images of the
human retina,
MedImg(21), No. 10, October 2002, pp. 1236-1243.
IEEE Top Reference.
0301
BibRef
Jelinek, H.F.[Herbert F.],
Cree, M.J.[Michael J.],
Leandro, J.J.G.[Jorge J. G.],
Soares, J.V.B.[João V. B.],
Cesar, R.M.[Roberto M.],
Luckie, A.,
Automated segmentation of retinal blood vessels and identification of
proliferative diabetic retinopathy,
JOSA-A(24), No. 5, May 2007, pp. 1448-1456.
WWW Link.
0801
BibRef
Niemeijer, M.,
Abramoff, M.D.,
van Ginneken, B.,
Information Fusion for Diabetic Retinopathy CAD in Digital Color Fundus
Photographs,
MedImg(28), No. 5, May 2009, pp. 775-785.
IEEE DOI
0905
BibRef
Agurto, C.,
Murray, V.,
Barriga, E.,
Murillo, S.,
Pattichis, M.,
Davis, H.,
Russell, S.,
Abramoff, M.,
Soliz, P.,
Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection,
MedImg(29), No. 2, February 2010, pp. 502-512.
IEEE DOI
1002
BibRef
Zhang, B.[Bob],
Wu, X.Q.[Xiang-Qian],
You, J.[Jane],
Li, Q.[Qin],
Karray, F.[Fakhri],
Detection of microaneurysms using multi-scale correlation coefficients,
PR(43), No. 6, June 2010, pp. 2237-2248.
Elsevier DOI
1003
Computer-aided diagnosis (CAD); Diabetic retinopathy (DR); Multi-scale
correlation filtering; Microaneurysm (red lesion) detection
BibRef
Patil, B.M.,
Joshi, R.C.[Ramesh C.],
Toshniwal, D.[Durga],
Classification of type-2 diabetic patients by using Apriori and
predictive Apriori,
IJCVR(2), No. 3, 2011, pp. 254-265.
DOI Link
1110
BibRef
Qureshi, R.J.[Rashid Jalal],
Kovacs, L.[Laszlo],
Harangi, B.[Balazs],
Nagy, B.[Brigitta],
Peto, T.[Tunde],
Hajdu, A.[Andras],
Combining algorithms for automatic detection of optic disc and macula
in fundus images,
CVIU(116), No. 1, January 2012, pp. 138-145.
Elsevier DOI
1112
Diabetic retinopathy; Macula detection; Optic disc detection; Retinal imaging
BibRef
Akram, M.U.[M. Usman],
Khalid, S.[Shehzad],
Khan, S.A.[Shoab A.],
Identification and classification of microaneurysms for early detection
of diabetic retinopathy,
PR(46), No. 1, January 2013, pp. 107-116.
Elsevier DOI
1209
Medical image processing; Diabetic retinopathy; Microaneurysms;
Classification; m-Mediods
BibRef
Tavakoli, M.[Meysam],
Shahri, R.P.[Reza Pourreza],
Pourreza, H.[Hamidreza],
Mehdizadeh, A.[Alireza],
Banaee, T.[Touka],
Toosi, M.H.B.[Mohammad Hosein Bahreini],
A complementary method for automated detection of microaneurysms in
fluorescein angiography fundus images to assess diabetic retinopathy,
PR(46), No. 10, October 2013, pp. 2740-2753.
Elsevier DOI
1306
Computer aided diagnosis; Diabetic retinopathy; Fluorescein
angiography; Fundus images; Radon transform; Microaneurysms
BibRef
Ranamuka, N.G.,
Meegama, R.G.N.,
Detection of hard exudates from diabetic retinopathy images
using fuzzy logic,
IET-PR(7), No. 2, 2013, pp. -.
DOI Link
1307
BibRef
Franklin, S.W.,
Rajan, S.E.,
Diagnosis of diabetic retinopathy by employing image processing
technique to detect exudates in retinal images,
IET-IPR(8), No. 10, October 2014, pp. 601-609.
DOI Link
1411
biomedical transducers
BibRef
Magdy, E.[Eman],
Ibrahim, M.[Mohamed],
Nguyen, Q.D.[Quan D.],
Fahmy, A.S.[Ahmed S.],
Computer-aided analysis of fluorescein angiograms using colour
leakage maps,
IET-IPR(9), No. 6, 2015, pp. 486-495.
DOI Link
1507
diseases
BibRef
Magdy, E.[Eman],
Agbedia, O.O.[Owhofasa O.],
Ibrahim, M.[Mohamed],
Nguyen, Q.D.[Quan D.],
Fahmy, A.S.[Ahmed S.],
Quantitative assessment of Diabetic Macular Edema using fluorescein
leakage maps,
ICIP12(2833-2836).
IEEE DOI
1302
BibRef
Navarro, P.J.[Pedro J.],
Alonso, D.[Diego],
Stathis, K.[Kostas],
Automatic detection of microaneurysms in diabetic retinopathy fundus
images using the L*a*b color space,
JOSA-A(33), No. 1, January 2016, pp. 74-83.
DOI Link
1601
Digital image processing
BibRef
Seoud, L.,
Hurtut, T.,
Chelbi, J.,
Cheriet, F.,
Langlois, J.M.P.,
Red Lesion Detection Using Dynamic Shape Features for Diabetic
Retinopathy Screening,
MedImg(35), No. 4, April 2016, pp. 1116-1126.
IEEE DOI
1604
biomedical optical imaging
BibRef
Shu, T.,
Zhang, B.,
Tang, Y.Y.,
Using K-NN with weights to detect diabetes mellitus based on genetic
algorithm feature selection,
ICWAPR16(12-17)
IEEE DOI
1611
Diabetes
BibRef
Zhao, Y.,
Zheng, Y.,
Liu, Y.,
Yang, J.,
Zhao, Y.,
Chen, D.,
Wang, Y.,
Intensity and Compactness Enabled Saliency Estimation for Leakage
Detection in Diabetic and Malarial Retinopathy,
MedImg(36), No. 1, January 2017, pp. 51-63.
IEEE DOI
1701
Diabetes
BibRef
Yoon, S.Y.[Soo-Young],
Park, S.[Soonchan],
Kim, H.G.[Hyug-Gi],
Park, T.H.[Tae-Hee],
Kim, S.M.[Sun Mi],
Hwang, Y.C.[You-Cheol],
Kim, G.Y.[Gou Young],
Ryu, C.W.[Chang-Woo],
Yoon, T.Y.[Tai-Young],
Jahng, G.H.[Geon-Ho],
Quantitative susceptibility mapping in a diabetes mellitus rat model:
Iron accumulation in the brain,
IJIST(27), No. 3, 2017, pp. 238-247.
DOI Link
1708
quantitative susceptibility mapping, diabetes rat, brain, , iron, accumulation
BibRef
Vogl, W.D.,
Waldstein, S.M.,
Gerendas, B.S.,
Schmidt-Erfurth, U.,
Langs, G.,
Predicting Macular Edema Recurrence from Spatio-Temporal Signatures
in Optical Coherence Tomography Images,
MedImg(36), No. 9, September 2017, pp. 1773-1783.
IEEE DOI
1709
biomedical optical imaging, diseases, eye,
optical tomography, SD-OCT scans,
branch retinal vein occlusion,
macular edema recurrence prediction,
retinal thickness features, Veins,
BibRef
de la Torre, J.[Jordi],
Puig, D.[Domenec],
Valls, A.[Aida],
Weighted kappa loss function for multi-class classification of
ordinal data in deep learning,
PRL(105), 2018, pp. 144-154.
Elsevier DOI
1804
Deep learning, Convolutional neural networks,
Supervised learning, Computer vision, Diabetic retinopathy, Weighted kappa
BibRef
Zhou, L.[Lei],
Zhao, Y.[Yu],
Yang, J.[Jie],
Yu, Q.[Qi],
Xu, X.[Xun],
Deep multiple instance learning for automatic detection of diabetic
retinopathy in retinal images,
IET-IPR(12), No. 4, April 2018, pp. 563-571.
DOI Link
1804
BibRef
Ghosh, S.K.[Swarup Kr],
Ghosh, A.[Anupam],
Chakrabarti, A.[Amlan],
VEA: Vessel Extraction Algorithm by Active Contour Model and a Novel
Wavelet Analyzer for Diabetic Retinopathy Detection,
IJIG(18), No. 02, 2018, pp. 1850008.
DOI Link
1804
BibRef
Kar, S.S.[Sudeshna Sil],
Maity, S.P.[Santi P.],
Gradation of diabetic retinopathy on reconstructed image using
compressed sensing,
IET-IPR(12), No. 11, November 2018, pp. 1956-1963.
DOI Link
1810
BibRef
Xiao, D.[Di],
Bhuiyan, A.[Alauddin],
Frost, S.[Shaun],
Vignarajan, J.[Janardhan],
Tay-Kearney, M.L.[Mei-Ling],
Kanagasingam, Y.[Yogesan],
Major automatic diabetic retinopathy screening systems and related core
algorithms: a review,
MVA(30), No. 3, April 2019, pp. 423-446.
Springer DOI
1906
BibRef
Leeza, M.[Mona],
Farooq, H.[Humera],
Detection of severity level of diabetic retinopathy using Bag of
features model,
IET-CV(13), No. 5, August 2019, pp. 523-530.
DOI Link
1908
BibRef
Verma, K.[Kesari],
Singh, B.K.[Bikesh Kumar],
Agrawal, N.[Neelam],
Non-invasive technique of diabetes detection using iris images,
IJCVR(9), No. 4, 2019, pp. 351-367.
DOI Link
1908
BibRef
Shankar, G.S.[G. Siva],
Manikandan, K.,
Diagnosis of diabetes diseases using optimized fuzzy rule set by grey
wolf optimization,
PRL(125), 2019, pp. 432-438.
Elsevier DOI
1909
Grey wolf optimization, Ant colony optimization, Fuzzy rules
BibRef
Khansari, M.M.,
Zhang, J.,
Qiao, Y.,
Gahm, J.K.,
Sarabi, M.S.,
Kashani, A.H.,
Shi, Y.,
Automated Deformation-Based Analysis of 3D Optical Coherence
Tomography in Diabetic Retinopathy,
MedImg(39), No. 1, January 2020, pp. 236-245.
IEEE DOI
2001
Optical coherence tomography, 3D image registration,
diabetic retinopathy, tensor-based morphometry
BibRef
Wang, J.L.[Jia-Liang],
Luo, J.[Jianxu],
Liu, B.[Bin],
Feng, R.[Rui],
Lu, L.[Lina],
Zou, H.[Haidong],
Automated diabetic retinopathy grading and lesion detection based on
the modified R-FCN object-detection algorithm,
IET-CV(14), No. 1, February 2020, pp. 1-8.
DOI Link
2002
BibRef
Li, X.,
Hu, X.,
Yu, L.,
Zhu, L.,
Fu, C.,
Heng, P.,
CANet: Cross-Disease Attention Network for Joint Diabetic Retinopathy
and Diabetic Macular Edema Grading,
MedImg(39), No. 5, May 2020, pp. 1483-1493.
IEEE DOI
2005
Diabetes, Task analysis, Feature extraction, Retinopathy,
Hemorrhaging, Biomedical imaging, Diabetic retinopathy,
attention mechanism
BibRef
Mousavi, E.[Elahe],
Kafieh, R.[Rahele],
Rabbani, H.[Hossein],
Classification of dry age-related macular degeneration and diabetic
macular oedema from optical coherence tomography images using
dictionary learning,
IET-IPR(14), No. 8, 19 June 2020, pp. 1571-1579.
DOI Link
2005
BibRef
Shankar, K.,
Sait, A.R.W.[Abdul Rahaman Wahab],
Gupta, D.[Deepak],
Lakshmanaprabu, S.K.,
Khanna, A.[Ashish],
Pandey, H.M.[Hari Mohan],
Automated detection and classification of fundus diabetic retinopathy
images using synergic deep learning model,
PRL(133), 2020, pp. 210-216.
Elsevier DOI
2005
Deep learning, Classification, Diabetic retinopathy,
Messidor dataset, Synergic deep learning
BibRef
Samanta, A.[Abhishek],
Saha, A.[Aheli],
Satapathy, S.C.[Suresh Chandra],
Fernandes, S.L.[Steven Lawrence],
Zhang, Y.D.[Yo-Dong],
Automated detection of diabetic retinopathy using convolutional
neural networks on a small dataset,
PRL(135), 2020, pp. 293-298.
Elsevier DOI
2006
Diabetic Retinopathy, CNN architecture, Colour fundus photography
BibRef
Zhao, Z.,
Zhang, K.,
Hao, X.,
Tian, J.,
Chua, M.C.H.[M. C. Heng],
Chen, L.,
Xu, X.,
BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading,
ICIP19(1385-1389)
IEEE DOI
1910
BibRef
Eladawi, N.,
Elmogy, M.,
Ghazal, M.,
Fraiwan, L.,
Aboelfetouh, A.,
Riad, A.,
Sandhu, H.,
Keynton, R.,
El-Baz, A.,
Early Signs Detection of Diabetic Retinopathy Using Optical Coherence
Tomography Angiography Scans Based on 3D Multi-Path Convolutional
Neural Network,
ICIP19(1390-1394)
IEEE DOI
1910
Early Diagnosis of DR, OCTA, Multi-path 3D CNN,
Blood Vessels Segmentation, Retinal Vasculature Features
BibRef
Kind, A.[Adrian],
Azzopardi, G.[George],
An Explainable AI-Based Computer Aided Detection System for Diabetic
Retinopathy Using Retinal Fundus Images,
CAIP19(I:457-468).
Springer DOI
1909
BibRef
Smailagic, A.[Asim],
Sharan, A.[Anupma],
Costa, P.[Pedro],
Galdran, A.[Adrian],
Gaudio, A.[Alex],
Campilho, A.[Aurélio],
Learned Pre-processing for Automatic Diabetic Retinopathy Detection on
Eye Fundus Images,
ICIAR19(II:362-368).
Springer DOI
1909
BibRef
Xiao, Q.[Qiqi],
Zou, J.[Jiaxu],
Yang, M.[Muqiao],
Gaudio, A.[Alex],
Kitani, K.[Kris],
Smailagic, A.[Asim],
Costa, P.[Pedro],
Xu, M.[Min],
Improving Lesion Segmentation for Diabetic Retinopathy Using
Adversarial Learning,
ICIAR19(II:333-344).
Springer DOI
1909
BibRef
Hernández, A.[Abián],
Arteaga-Marrero, N.[Natalia],
Villa, E.[Enrique],
Fabelo, H.[Himar],
Callicó, G.M.[Gustavo M.],
Ruiz-Alzola, J.[Juan],
Automatic Segmentation Based on Deep Learning Techniques for Diabetic
Foot Monitoring Through Multimodal Images,
CIAP19(II:414-424).
Springer DOI
1909
BibRef
Eladawi, N.,
Elmogy, M.,
Fraiwan, L.,
Pichi, F.,
Ghazal, M.,
Aboelfetouh, A.,
Riad, A.,
Keynton, R.,
Schaal, S.,
El-Baz, A.,
Early Diagnosis of Diabetic Retinopathy in OCTA Images Based on Local
Analysis of Retinal Blood Vessels and Foveal Avascular Zone,
ICPR18(3886-3891)
IEEE DOI
1812
Retina, Feature extraction, Biomedical imaging, Blood vessels,
Image segmentation, Support vector machines
BibRef
Colomer, A.[Adrián],
Ruiz, P.[Pablo],
Naranjo, V.[Valery],
Molina, R.[Rafael],
Katsaggelos, A.K.[Aggelos K.],
Hard Exudate Detection Using Local Texture Analysis and Gaussian
Processes,
ICIAR18(639-649).
Springer DOI
1807
BibRef
Neira-Tovar, L.[Leticia],
Rodriguez, I.C.[Ivan Castilla],
The Use of Virtual and Augmented Reality to Prevent the Physical
Effects Caused by Diabetes Melitus Type 2: An Integrative Review,
VAMR18(II: 126-133).
Springer DOI
1807
BibRef
Bourouis, S.[Sami],
Zaguia, A.[Atef],
Bouguila, N.[Nizar],
Hybrid Statistical Framework for Diabetic Retinopathy Detection,
ICIAR18(687-694).
Springer DOI
1807
BibRef
Melo, T.[Tânia],
Mendonça, A.M.[Ana Maria],
Campilho, A.[Aurélio],
Creation of Retinal Mosaics for Diabetic Retinopathy Screening: A
Comparative Study,
ICIAR18(669-678).
Springer DOI
1807
BibRef
Gondal, W.M.,
Köhler, J.M.,
Grzeszick, R.,
Fink, G.A.,
Hirsch, M.,
Weakly-supervised localization of diabetic retinopathy lesions in
retinal fundus images,
ICIP17(2069-2073)
IEEE DOI
1803
Biomedical imaging, Cams, Diabetes, Lesions, Retina, Retinopathy,
Training, deep learning, diabetic retinopathy, lesion detection,
weakly-supervised object localization
BibRef
Komi, M.,
Li, J.[Jun],
Zhai, Y.X.[Yong-Xin],
Zhang, X.G.[Xian-Guo],
Application of data mining methods in diabetes prediction,
ICIVC17(1006-1010)
IEEE DOI
1708
Biological system modeling, Biomedical imaging, Blood,
Computational modeling, Diabetes, Diseases,
Support vector machines, classification, data mining, diabetes, prediction
BibRef
Costa, P.,
Campilho, A.,
Convolutional bag of words for diabetic retinopathy detection from
eye fundus images,
MVA17(165-168)
DOI Link
1708
Encoding, Feature extraction, Lesions, Pathology, Retina, Retinopathy, Visualization
BibRef
Massich, J.,
Rastgoo, M.,
Lemaître, G.,
Cheung, C.Y.,
Wong, T.Y.,
Sidibé, D.,
Mériaudeau, F.,
Classifying DME vs normal SD-OCT volumes: A review,
ICPR16(1297-1302)
IEEE DOI
1705
Diabetes, Feature extraction, Histograms, Pathology,
Principal component analysis, Retina, Testing,
Diabetic Macular Edema (DME), Machine Learning (ML),
Spectral Domain OCT (SD-OCT), benchmark
BibRef
Soomro, T.A.[Toufique A.],
Gao, J.B.[Jun-Bin],
Khan, M.A.U.[Mohammad A.U.],
Khan, T.M.[Tariq M.],
Paul, M.[Manoranjan],
Role of Image Contrast Enhancement Technique for Ophthalmologist as
Diagnostic Tool for Diabetic Retinopathy,
DICTA16(1-8)
IEEE DOI
1701
Biomedical imaging
BibRef
Kaur, A.,
Kaur, P.,
An integrated approach for Diabetic Retinopathy exudate segmentation
by using Genetic Algorithm and Switching Median Filter,
ICIVC16(119-123)
IEEE DOI
1610
ant colony optimisation
BibRef
Agurto, C.,
Chek, V.,
Edwards, A.,
Jarry, Z.,
Barriga, S.,
Simon, J.,
Soliz, P.,
A thermoregulation model to detect diabetic peripheral neuropathy,
Southwest16(13-16)
IEEE DOI
1605
Biological system modeling
BibRef
Laaksonen, L.[Lauri],
Hannuksela, A.,
Claridge, E.[Ela],
Fält, P.[Pauli],
Hauta-Kasari, M.[Markku],
Uusitalo, H.[Hannu],
Lensu, L.[Lasse],
Evaluation of feature sensitivity to training data inaccuracy in
detection of retinal lesions,
IPTA16(1-6)
IEEE DOI
1703
edge detection
BibRef
Nguyen, U.,
Laaksonen, L.[Lauri],
Uusitalo, H.[Hannu],
Lensu, L.[Lasse],
Reconstruction of retinal spectra from RGB data using a RBF network,
IPTA16(1-6)
IEEE DOI
1703
image colour analysis
BibRef
Fält, P.[Pauli],
Yamaguchi, M.[Masahiro],
Murakami, Y.[Yuri],
Laaksonen, L.[Lauri],
Lensu, L.[Lasse],
Claridge, E.[Ela],
Hauta-Kasari, M.[Markku],
Uusitalo, H.[Hannu],
Multichannel Spectral Image Enhancement for Visualizing Diabetic
Retinopathy Lesions,
ICISP14(52-60).
Springer DOI
1406
BibRef
Velu, C.M.,
Kashwan, K.R.,
Multi-Level Counter Propagation Network for diabetes classification,
ICSIPR13(190-194).
IEEE DOI
1304
BibRef
Akram, M.U.[M. Usman],
Tariq, A.[Anam],
Khan, S.A.[Shoab A.],
Detection of Neovascularization for Screening of Proliferative Diabetic
Retinopathy,
ICIAR12(II: 372-379).
Springer DOI
1206
BibRef
Carranza, C.[Cesar],
Murray, V.[Victor],
Pattichis, M.[Marios],
Barriga, E.S.[E. Simon],
Multiscale AM-FM decompositions with GPU acceleration for diabetic
retinopathy screening,
Southwest12(121-124).
IEEE DOI
1205
BibRef
Yu, H.G.[Hong-Gang],
Agurto, C.[Carla],
Barriga, S.[Simon],
Nemeth, S.C.[Sheila C.],
Soliz, P.[Peter],
Zamora, G.[Gilberto],
Automated image quality evaluation of retinal fundus photographs in
diabetic retinopathy screening,
Southwest12(125-128).
IEEE DOI
1205
BibRef
Poddar, S.[Sunrita],
Jha, B.K.[Bibhash Kumar],
Chakraborty, C.[Chandan],
Quantitative clinical marker extraction from colour fundus images for
non-proliferative Diabetic Retinopathy grading,
ICIIP11(1-6).
IEEE DOI
1112
BibRef
Yano, V.[Vitor],
Ferrari, G.[Giselle],
Zimmer, A.[Alessandro],
Using the Pupillary Reflex as a Diabetes Occurrence Screening Aid Tool
through Neural Networks,
ICIAR11(II: 40-47).
Springer DOI
1106
BibRef
Ferreira, A.[Ana],
Morgado, A.M.[António Miguel],
Silva, J.S.[José Silvestre],
Automatic Corneal Nerves Recognition for Earlier Diagnosis and
Follow-Up of Diabetic Neuropathy,
ICIAR10(II: 60-69).
Springer DOI
1006
BibRef
Hann, C.E.,
Narbot, M.,
MacAskill, M.,
Diabetic Retinopathy detection using geometrical techniques related to
the underlying physiology,
IVCNZ10(1-8).
IEEE DOI
1203
BibRef
Ravishankar, S.[Saiprasad],
Jain, A.[Arpit],
Mittal, A.[Anurag],
Automated feature extraction for early detection of diabetic
retinopathy in fundus images,
CVPR09(210-217).
IEEE DOI
0906
BibRef
Fält, P.[Pauli],
Hiltunen, J.[Jouni],
Hauta-Kasari, M.[Markku],
Sorri, I.[Iiris],
Kalesnykiene, V.[Valentina],
Uusitalo, H.[Hannu],
Extending Diabetic Retinopathy Imaging from Color to Spectra,
SCIA09(149-158).
Springer DOI
0906
BibRef
Kauppi, T.,
Kalesnykiene, V.,
Kamarainen, J.K.,
Lensu, L.,
Sorri, I.,
Raninen, A.,
Voutilainen, R.,
Uusitalo, H.,
Kalviainen, H.,
Pietila, J.,
The DIARETDB1 diabetic retinopathy database and evaluation protocol,
BMVC07(xx-yy).
PDF File.
0709
Dataset, Retina.
BibRef
Estabridis, K.[Katia],
de Figueiredo, R.J.P.[Rui J. P.],
Automatic Detection and Diagnosis of Diabetic Retinopathy,
ICIP07(II: 445-448).
IEEE DOI
0709
BibRef
Xu, X.Y.[Xin-Yu],
Li, B.X.[Bao-Xin],
Florez, J.F.[Jose F.],
Li, H.K.[Helen K.],
Simulation of Diabetic Retinopathy Neovascularization in Color Digital
Fundus Images,
ISVC06(I: 421-433).
Springer DOI
0611
BibRef
Zhang, X.H.[Xiao-Hui],
Chutatape, O.[Opas],
Top-Down and Bottom-Up Strategies in Lesion Detection of Background
Diabetic Retinopathy,
CVPR05(II: 422-428).
IEEE DOI
0507
BibRef
Earlier:
Detection and classification of bright lesions in color fundus images,
ICIP04(I: 139-142).
IEEE DOI
0505
BibRef
Osareh, A.[Alireza],
Shadgar, B.[Bita],
Markham, R.[Richard],
Comparative Pixel-Level Exudate Recognition in Colour Retinal Images,
ICIAR05(894-902).
Springer DOI
0509
BibRef
Osareh, A.,
Mirmehdi, M.,
Thomas, B.,
Markham, R.,
Comparison of colour spaces for optic disc localisation in retinal
images,
ICPR02(I: 743-746).
IEEE DOI
0211
BibRef
Earlier:
Classification and Localisation of Diabetic-Related Eye Disease,
ECCV02(IV: 502 ff.).
Springer DOI
0205
BibRef
Butikova, J.,
Bocchi, L.,
Freivalds, T.,
Texture analysis and optical anisotropy measurements of leukocytes for
early diagnostics of diabetes mellitus,
ICIP03(I: 1081-1084).
IEEE DOI
0312
BibRef
Byrne, M.J.,
Graham, J.,
Application of Model Based Image Interpretation Methods
of Diabetic Neuropathy,
ECCV96(II:272-282).
Springer DOI
BibRef
9600
Chapter on Medical Applications, CAT, MRI, Ultrasound, Heart Models, Brain Models continues in
Macular Degeneration Detection, Retinal Analysis Application .