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Journal Abstract Search

590 related items for PubMed ID: 19586814

  • 1. 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
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  • 4. 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
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  • 5. 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
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  • 6. 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 10; 51(20):4858-66. PubMed ID: 22781265
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  • 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 21; 52(24):7385-96. PubMed ID: 18065845
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  • 9. 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 21; 53(6):1084-98. PubMed ID: 16761836
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  • 11. Automated feature extraction in color retinal images by a model based approach.
    Li H, Chutatape O.
    IEEE Trans Biomed Eng; 2004 Feb 21; 51(2):246-54. PubMed ID: 14765697
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  • 12. Neural network based detection of hard exudates in retinal images.
    García M, Sánchez CI, López MI, Abásolo D, Hornero R.
    Comput Methods Programs Biomed; 2009 Jan 21; 93(1):9-19. PubMed ID: 18778869
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  • 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 21; 27(1):11-8. PubMed ID: 18270057
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  • 14. Automatic detection of microaneurysms in color fundus images.
    Walter T, Massin P, Erginay A, Ordonez R, Jeulin C, Klein JC.
    Med Image Anal; 2007 Dec 21; 11(6):555-66. PubMed ID: 17950655
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  • 15. 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 21; 59(8):2244-53. PubMed ID: 22665502
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  • 16. Content-based medical image classification using a new hierarchical merging scheme.
    Pourghassem H, Ghassemian H.
    Comput Med Imaging Graph; 2008 Dec 21; 32(8):651-61. PubMed ID: 18789648
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  • 17. Optic nerve head segmentation.
    Lowell J, Hunter A, Steel D, Basu A, Ryder R, Fletcher E, Kennedy L.
    IEEE Trans Med Imaging; 2004 Feb 21; 23(2):256-64. PubMed ID: 14964569
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  • 18. Automated identification of diabetic retinal exudates in digital colour images.
    Osareh A, Mirmehdi M, Thomas B, Markham R.
    Br J Ophthalmol; 2003 Oct 21; 87(10):1220-3. PubMed ID: 14507751
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  • 19. 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 21; 36(3):213-20. PubMed ID: 20219404
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  • 20. A multiple-instance learning framework for diabetic retinopathy screening.
    Quellec G, Lamard M, Abràmoff MD, Decencière E, Lay B, Erginay A, Cochener B, Cazuguel G.
    Med Image Anal; 2012 Aug 21; 16(6):1228-40. PubMed ID: 22850462
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