BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

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]
    of 22.