425 related articles for article (PubMed ID: 12585705)
1. A contribution of image processing to the diagnosis of diabetic retinopathy--detection of exudates in color fundus images of the human retina.
Walter T; Klein JC; Massin P; Erginay A
IEEE Trans Med Imaging; 2002 Oct; 21(10):1236-43. PubMed ID: 12585705
[TBL] [Abstract][Full Text] [Related]
2. Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy.
Narasimha-Iyer H; Can A; Roysam B; Stewart CV; Tanenbaum HL; Majerovics A; Singh H
IEEE Trans Biomed Eng; 2006 Jun; 53(6):1084-98. PubMed ID: 16761836
[TBL] [Abstract][Full Text] [Related]
3. A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis.
Sánchez CI; Hornero R; López MI; Aboy M; Poza J; Abásolo D
Med Eng Phys; 2008 Apr; 30(3):350-7. PubMed ID: 17556004
[TBL] [Abstract][Full Text] [Related]
4. Automated feature extraction in color retinal images by a model based approach.
Li H; Chutatape O
IEEE Trans Biomed Eng; 2004 Feb; 51(2):246-54. PubMed ID: 14765697
[TBL] [Abstract][Full Text] [Related]
5. Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, haemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy.
Dupas B; Walter T; Erginay A; Ordonez R; Deb-Joardar N; Gain P; Klein JC; Massin P
Diabetes Metab; 2010 Jun; 36(3):213-20. PubMed ID: 20219404
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. Automated detection of exudates for diabetic retinopathy screening.
Fleming AD; Philip S; Goatman KA; Williams GJ; Olson JA; Sharp PF
Phys Med Biol; 2007 Dec; 52(24):7385-96. PubMed ID: 18065845
[TBL] [Abstract][Full Text] [Related]
9. Ridge-based vessel segmentation in color images of the retina.
Staal J; Abràmoff MD; Niemeijer M; Viergever MA; van Ginneken B
IEEE Trans Med Imaging; 2004 Apr; 23(4):501-9. PubMed ID: 15084075
[TBL] [Abstract][Full Text] [Related]
10. A computational-intelligence-based approach for detection of exudates in diabetic retinopathy images.
Osareh A; Shadgar B; Markham R
IEEE Trans Inf Technol Biomed; 2009 Jul; 13(4):535-45. PubMed ID: 19586814
[TBL] [Abstract][Full Text] [Related]
11. Detection of hard exudates in retinal images using a radial basis function classifier.
García M; Sánchez CI; Poza J; López MI; Hornero R
Ann Biomed Eng; 2009 Jul; 37(7):1448-63. PubMed ID: 19430906
[TBL] [Abstract][Full Text] [Related]
12. Optic nerve head segmentation.
Lowell J; Hunter A; Steel D; Basu A; Ryder R; Fletcher E; Kennedy L
IEEE Trans Med Imaging; 2004 Feb; 23(2):256-64. PubMed ID: 14964569
[TBL] [Abstract][Full Text] [Related]
13. Optic disc detection from normalized digital fundus images by means of a vessels' direction matched filter.
Youssif AR; Ghalwash AZ; Ghoneim AR
IEEE Trans Med Imaging; 2008 Jan; 27(1):11-8. PubMed ID: 18270057
[TBL] [Abstract][Full Text] [Related]
14. Automatic detection of red lesions in digital color fundus photographs.
Niemeijer M; van Ginneken B; Staal J; Suttorp-Schulten MS; Abràmoff MD
IEEE Trans Med Imaging; 2005 May; 24(5):584-92. PubMed ID: 15889546
[TBL] [Abstract][Full Text] [Related]
15. Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods.
Sopharak A; Uyyanonvara B; Barman S; Williamson TH
Comput Med Imaging Graph; 2008 Dec; 32(8):720-7. PubMed ID: 18930631
[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. Automatic tracing of optic disc and exudates from color fundus images using fixed and variable thresholds.
Reza AW; Eswaran C; Hati S
J Med Syst; 2009 Feb; 33(1):73-80. PubMed ID: 19238899
[TBL] [Abstract][Full Text] [Related]
18. Segmentation of the optic disk in color eye fundus images using an adaptive morphological approach.
Welfer D; Scharcanski J; Kitamura CM; Dal Pizzol MM; Ludwig LW; Marinho DR
Comput Biol Med; 2010 Feb; 40(2):124-37. PubMed ID: 20045104
[TBL] [Abstract][Full Text] [Related]
19. Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels.
Hoover A; Goldbaum M
IEEE Trans Med Imaging; 2003 Aug; 22(8):951-8. PubMed ID: 12906249
[TBL] [Abstract][Full Text] [Related]
20. Detection of optic disc in retinal images by means of a geometrical model of vessel structure.
Foracchia M; Grisan E; Ruggeri A
IEEE Trans Med Imaging; 2004 Oct; 23(10):1189-95. PubMed ID: 15493687
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]