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 *

331 related articles for article (PubMed ID: 18930631)

  • 21. Computer-based detection of diabetes retinopathy stages using digital fundus images.
    Acharya UR; Lim CM; Ng EY; Chee C; Tamura T
    Proc Inst Mech Eng H; 2009 Jul; 223(5):545-53. PubMed ID: 19623908
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Automatic identification of diabetic maculopathy stages using fundus images.
    Nayak J; Bhat PS; Acharya UR
    J Med Eng Technol; 2009; 33(2):119-29. PubMed ID: 19205991
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Detection of exudates in retinal images using a pure splitting technique.
    Jaafar HF; Nandi AK; Al-Nuaimy W
    Annu Int Conf IEEE Eng Med Biol Soc; 2010; 2010():6745-8. PubMed ID: 21095830
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Extraction and reconstruction of retinal vasculature.
    Ahmad Fadzil MH; Izhar LI; Venkatachalam PA; Karunakar TV
    J Med Eng Technol; 2007; 31(6):435-42. PubMed ID: 17994417
    [TBL] [Abstract][Full Text] [Related]  

  • 25. 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; 11(6):555-66. PubMed ID: 17950655
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Automated detection of exudates and macula for grading of diabetic macular edema.
    Akram MU; Tariq A; Khan SA; Javed MY
    Comput Methods Programs Biomed; 2014 Apr; 114(2):141-52. PubMed ID: 24548898
    [TBL] [Abstract][Full Text] [Related]  

  • 27. 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]  

  • 28. Automated assessment of diabetic retinal image quality based on clarity and field definition.
    Fleming AD; Philip S; Goatman KA; Olson JA; Sharp PF
    Invest Ophthalmol Vis Sci; 2006 Mar; 47(3):1120-5. PubMed ID: 16505050
    [TBL] [Abstract][Full Text] [Related]  

  • 29. 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]  

  • 30. 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]  

  • 31. 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]  

  • 32. Diabetic retinopathy: the unmet needs for screening and a review of potential solutions.
    Sinclair SH
    Expert Rev Med Devices; 2006 May; 3(3):301-13. PubMed ID: 16681452
    [TBL] [Abstract][Full Text] [Related]  

  • 33. 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]  

  • 34. Diabetic retinopathy screening using digital non-mydriatic fundus photography and automated image analysis.
    Hansen AB; Hartvig NV; Jensen MS; Borch-Johnsen K; Lund-Andersen H; Larsen M
    Acta Ophthalmol Scand; 2004 Dec; 82(6):666-72. PubMed ID: 15606461
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Computer aided quantification for retinal lesions in patients with moderate and severe non-proliferative diabetic retinopathy: a retrospective cohort study.
    Wu H; Zhang X; Geng X; Dong J; Zhou G
    BMC Ophthalmol; 2014 Oct; 14():126. PubMed ID: 25359611
    [TBL] [Abstract][Full Text] [Related]  

  • 36. A novel method for retinal exudate segmentation using signal separation algorithm.
    Imani E; Pourreza HR
    Comput Methods Programs Biomed; 2016 Sep; 133():195-205. PubMed ID: 27393810
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Fully automated diabetic retinopathy screening using morphological component analysis.
    Imani E; Pourreza HR; Banaee T
    Comput Med Imaging Graph; 2015 Jul; 43():78-88. PubMed ID: 25863517
    [TBL] [Abstract][Full Text] [Related]  

  • 38. 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]  

  • 39. A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images.
    Liu Q; Zou B; Chen J; Ke W; Yue K; Chen Z; Zhao G
    Comput Med Imaging Graph; 2017 Jan; 55():78-86. PubMed ID: 27665058
    [TBL] [Abstract][Full Text] [Related]  

  • 40. TOSCA-Imaging--developing Internet based image processing software for screening and diagnosis of diabetic retinopathy.
    Hejlesen O; Ege B; Englmeier KH; Aldington S; McCanna L; Bek T
    Stud Health Technol Inform; 2004; 107(Pt 1):222-6. PubMed ID: 15360807
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 17.