154 related articles for article (PubMed ID: 20703589)
1. A decision support system for automatic screening of non-proliferative diabetic retinopathy.
Reza AW; Eswaran C
J Med Syst; 2011 Feb; 35(1):17-24. PubMed ID: 20703589
[TBL] [Abstract][Full Text] [Related]
2. Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis.
Niemeijer M; van Ginneken B; Russell SR; Suttorp-Schulten MS; Abràmoff MD
Invest Ophthalmol Vis Sci; 2007 May; 48(5):2260-7. PubMed ID: 17460289
[TBL] [Abstract][Full Text] [Related]
3. Non-proliferative diabetic retinopathy symptoms detection and classification using neural network.
Al-Jarrah MA; Shatnawi H
J Med Eng Technol; 2017 Aug; 41(6):498-505. PubMed ID: 28786703
[TBL] [Abstract][Full Text] [Related]
4. Computer classification of nonproliferative diabetic retinopathy.
Lee SC; Lee ET; Wang Y; Klein R; Kingsley RM; Warn A
Arch Ophthalmol; 2005 Jun; 123(6):759-64. PubMed ID: 15955976
[TBL] [Abstract][Full Text] [Related]
5. Automated detection of dark and bright lesions in retinal images for early detection of diabetic retinopathy.
Akram UM; Khan SA
J Med Syst; 2012 Oct; 36(5):3151-62. PubMed ID: 22090037
[TBL] [Abstract][Full Text] [Related]
6. Detection of Hard Exudates in Colour Fundus Images Using Fuzzy Support Vector Machine-Based Expert System.
Jaya T; Dheeba J; Singh NA
J Digit Imaging; 2015 Dec; 28(6):761-8. PubMed ID: 25822397
[TBL] [Abstract][Full Text] [Related]
7. Retinal images benchmark for the detection of diabetic retinopathy and clinically significant macular edema (CSME).
Noor-Ul-Huda M; Tehsin S; Ahmed S; Niazi FAK; Murtaza Z
Biomed Tech (Berl); 2019 May; 64(3):297-307. PubMed ID: 30055096
[TBL] [Abstract][Full Text] [Related]
8. Points of interest and visual dictionaries for automatic retinal lesion detection.
Rocha A; Carvalho T; Jelinek HF; Goldenstein S; Wainer J
IEEE Trans Biomed Eng; 2012 Aug; 59(8):2244-53. PubMed ID: 22665502
[TBL] [Abstract][Full Text] [Related]
9. Detection of Hard Exudates Using Evolutionary Feature Selection in Retinal Fundus Images.
Kadan AB; Subbian PS
J Med Syst; 2019 May; 43(7):209. PubMed ID: 31144041
[TBL] [Abstract][Full Text] [Related]
10. Detection and classification of retinal lesions for grading of diabetic retinopathy.
Usman Akram M; Khalid S; Tariq A; Khan SA; Azam F
Comput Biol Med; 2014 Feb; 45():161-71. PubMed ID: 24480176
[TBL] [Abstract][Full Text] [Related]
11. Automated System for Referral of Cotton-Wool Spots.
Naqvi SAG; Zafar HMF; Ul Haq I
Curr Diabetes Rev; 2018; 14(2):168-174. PubMed ID: 27908249
[TBL] [Abstract][Full Text] [Related]
12. Decision support system for the detection and grading of hard exudates from color fundus photographs.
Jaafar HF; Nandi AK; Al-Nuaimy W
J Biomed Opt; 2011 Nov; 16(11):116001. PubMed ID: 22112106
[TBL] [Abstract][Full Text] [Related]
13. Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images.
Köse C; Sevik U; Ikibaş C; Erdöl H
Comput Methods Programs Biomed; 2012 Aug; 107(2):274-93. PubMed ID: 21757250
[TBL] [Abstract][Full Text] [Related]
14. DREAM: diabetic retinopathy analysis using machine learning.
Roychowdhury S; Koozekanani DD; Parhi KK
IEEE J Biomed Health Inform; 2014 Sep; 18(5):1717-28. PubMed ID: 25192577
[TBL] [Abstract][Full Text] [Related]
15. Feature extraction and selection for the automatic detection of hard exudates in retinal images.
Garcia M; Hornero R; Sánchez CI; López MI; Diez A
Annu Int Conf IEEE Eng Med Biol Soc; 2007; 2007():4969-72. PubMed ID: 18003122
[TBL] [Abstract][Full Text] [Related]
16. Automated detection of exudates in colored retinal images for diagnosis of diabetic retinopathy.
Akram MU; Tariq A; Anjum MA; Javed MY
Appl Opt; 2012 Jul; 51(20):4858-66. PubMed ID: 22781265
[TBL] [Abstract][Full Text] [Related]
17. Retinal image analysis based on mixture models to detect hard exudates.
Sánchez CI; García M; Mayo A; López MI; Hornero R
Med Image Anal; 2009 Aug; 13(4):650-8. PubMed ID: 19539518
[TBL] [Abstract][Full Text] [Related]
18. Segmentation and classification of bright lesions to diagnose diabetic retinopathy in retinal images.
Santhi D; Manimegalai D; Parvathi S; Karkuzhali S
Biomed Tech (Berl); 2016 Aug; 61(4):443-53. PubMed ID: 27060730
[TBL] [Abstract][Full Text] [Related]
19. Automated lesion detectors in retinal fundus images.
Figueiredo IN; Kumar S; Oliveira CM; Ramos JD; Engquist B
Comput Biol Med; 2015 Nov; 66():47-65. PubMed ID: 26378502
[TBL] [Abstract][Full Text] [Related]
20. Diagnosis of diabetic retinopathy: automatic extraction of optic disc and exudates from retinal images using marker-controlled watershed transformation.
Reza AW; Eswaran C; Dimyati K
J Med Syst; 2011 Dec; 35(6):1491-501. PubMed ID: 20703768
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
