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 *

143 related articles for article (PubMed ID: 21901535)

  • 1. Detection of neovascularization in diabetic retinopathy.
    Hassan SS; Bong DB; Premsenthil M
    J Digit Imaging; 2012 Jun; 25(3):437-44. PubMed ID: 21901535
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 4. Detection of neovascularization based on fractal and texture analysis with interaction effects in diabetic retinopathy.
    Lee J; Zee BC; Li Q
    PLoS One; 2013; 8(12):e75699. PubMed ID: 24358105
    [TBL] [Abstract][Full Text] [Related]  

  • 5. 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]  

  • 6. 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]  

  • 7. Simple hybrid method for fine microaneurysm detection from non-dilated diabetic retinopathy retinal images.
    Sopharak A; Uyyanonvara B; Barman S
    Comput Med Imaging Graph; 2013; 37(5-6):394-402. PubMed ID: 23777979
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Retinal images benchmark for the detection of diabetic retinopathy and clinically significant macular edema (CSME).
    Noor-Ul-Huda M; Tehsin S; Ahmed S; Niazi FAK; Murtaza Z
    Biomed Tech (Berl); 2019 May; 64(3):297-307. PubMed ID: 30055096
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Detection of neovascularization in retinal images using multivariate m-Mediods based classifier.
    Usman Akram M; Khalid S; Tariq A; Younus Javed M
    Comput Med Imaging Graph; 2013; 37(5-6):346-57. PubMed ID: 23916066
    [TBL] [Abstract][Full Text] [Related]  

  • 10. 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]  

  • 11. 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]  

  • 12. 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]  

  • 13. 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]  

  • 14. Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques.
    Akyol K; Şen B; Bayır Ş
    Comput Math Methods Med; 2016; 2016():6814791. PubMed ID: 27110272
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification.
    Welikala RA; Dehmeshki J; Hoppe A; Tah V; Mann S; Williamson TH; Barman SA
    Comput Methods Programs Biomed; 2014 May; 114(3):247-61. PubMed ID: 24636803
    [TBL] [Abstract][Full Text] [Related]  

  • 16. 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]  

  • 17. A method to assist in the diagnosis of early diabetic retinopathy: Image processing applied to detection of microaneurysms in fundus images.
    Rosas-Romero R; Martínez-Carballido J; Hernández-Capistrán J; Uribe-Valencia LJ
    Comput Med Imaging Graph; 2015 Sep; 44():41-53. PubMed ID: 26245720
    [TBL] [Abstract][Full Text] [Related]  

  • 18. 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]  

  • 19. 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]  

  • 20. Automated segmentation of retinal blood vessels and identification of proliferative diabetic retinopathy.
    Jelinek HF; Cree MJ; Leandro JJ; Soares JV; Cesar RM; Luckie A
    J Opt Soc Am A Opt Image Sci Vis; 2007 May; 24(5):1448-56. PubMed ID: 17429492
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.