These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

166 related articles for article (PubMed ID: 20129850)

  • 1. Multiscale AM-FM methods for diabetic retinopathy lesion detection.
    Agurto C; Murray V; Barriga E; Murillo S; Pattichis M; Davis H; Russell S; Abramoff M; Soliz P
    IEEE Trans Med Imaging; 2010 Feb; 29(2):502-12. PubMed ID: 20129850
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. Classification of diabetic retinopathy images using multi-class multiple-instance learning based on color correlogram features.
    Venkatesan R; Chandakkar P; Li B; Li HK
    Annu Int Conf IEEE Eng Med Biol Soc; 2012; 2012():1462-5. PubMed ID: 23366177
    [TBL] [Abstract][Full Text] [Related]  

  • 4. 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]  

  • 5. Detection of retinal lesions in diabetic retinopathy: comparative evaluation of 7-field digital color photography versus red-free photography.
    Venkatesh P; Sharma R; Vashist N; Vohra R; Garg S
    Int Ophthalmol; 2015 Oct; 35(5):635-40. PubMed ID: 22961609
    [TBL] [Abstract][Full Text] [Related]  

  • 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; 51(20):4858-66. PubMed ID: 22781265
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. 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]  

  • 9. Detection of lesions in retina photographs based on the wavelet transform.
    Quellec G; Lamard M; Josselin PM; Cazuguel G; Cochener B; Roux C
    Conf Proc IEEE Eng Med Biol Soc; 2006; 2006():2618-21. PubMed ID: 17945729
    [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 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]  

  • 12. Discrimination of retinal images containing bright lesions using sparse coded features and SVM.
    Sidibé D; Sadek I; Mériaudeau F
    Comput Biol Med; 2015 Jul; 62():175-84. PubMed ID: 25935125
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. 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]  

  • 15. Multi-space clustering for segmentation of exudates in retinal color photographs.
    Ram K; Sivaswamy J
    Annu Int Conf IEEE Eng Med Biol Soc; 2009; 2009():1437-40. PubMed ID: 19963747
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Automatic detection of blood vessels in retinal images for diabetic retinopathy diagnosis.
    Raja DS; Vasuki S
    Comput Math Methods Med; 2015; 2015():419279. PubMed ID: 25810749
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. 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]  

  • 19. 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]  

  • 20. Automated detection of fundus photographic red lesions in diabetic retinopathy.
    Larsen M; Godt J; Larsen N; Lund-Andersen H; Sjølie AK; Agardh E; Kalm H; Grunkin M; Owens DR
    Invest Ophthalmol Vis Sci; 2003 Feb; 44(2):761-6. PubMed ID: 12556411
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 9.