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 *

137 related articles for article (PubMed ID: 24111079)

  • 1. Comparison of logistic regression and neural network classifiers in the detection of hard exudates in retinal images.
    Garcia M; Valverde C; Lopez MI; Poza J; Hornero R
    Annu Int Conf IEEE Eng Med Biol Soc; 2013; 2013():5891-4. PubMed ID: 24111079
    [TBL] [Abstract][Full Text] [Related]  

  • 2. 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]  

  • 3. 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]  

  • 4. 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; 93(1):9-19. PubMed ID: 18778869
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Automatic detection of red lesions in retinal images using a multilayer perceptron neural network.
    García M; Sánchez CI; López MI; Díez A; Hornero R
    Annu Int Conf IEEE Eng Med Biol Soc; 2008; 2008():5425-8. PubMed ID: 19163944
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Assessment of four neural network based classifiers to automatically detect red lesions in retinal images.
    García M; López MI; Alvarez D; Hornero R
    Med Eng Phys; 2010 Dec; 32(10):1085-93. PubMed ID: 20739211
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. 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]  

  • 9. 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]  

  • 10. 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]  

  • 11. Automatic image processing algorithm to detect hard exudates based on mixture models.
    Sánchez CI; Mayo A; García M; López MI; Hornero R
    Conf Proc IEEE Eng Med Biol Soc; 2006; 2006():4453-6. PubMed ID: 17945839
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Hard exudates segmentation based on learned initial seeds and iterative graph cut.
    Kusakunniran W; Wu Q; Ritthipravat P; Zhang J
    Comput Methods Programs Biomed; 2018 May; 158():173-183. PubMed ID: 29544783
    [TBL] [Abstract][Full Text] [Related]  

  • 13. 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]  

  • 14. 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]  

  • 15. A Novel Approach for Detection of Hard Exudates Using Random Forest Classifier.
    Pratheeba C; Singh NN
    J Med Syst; 2019 May; 43(7):180. PubMed ID: 31093787
    [TBL] [Abstract][Full Text] [Related]  

  • 16. An ensemble classification of exudates in color fundus images using an evolutionary algorithm based optimal features selection.
    Ullah H; Saba T; Islam N; Abbas N; Rehman A; Mehmood Z; Anjum A
    Microsc Res Tech; 2019 Apr; 82(4):361-372. PubMed ID: 30677193
    [TBL] [Abstract][Full Text] [Related]  

  • 17. 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]  

  • 18. Effective Fundus Image Decomposition for the Detection of Red Lesions and Hard Exudates to Aid in the Diagnosis of Diabetic Retinopathy.
    Romero-Oraá R; García M; Oraá-Pérez J; López-Gálvez MI; Hornero R
    Sensors (Basel); 2020 Nov; 20(22):. PubMed ID: 33207825
    [TBL] [Abstract][Full Text] [Related]  

  • 19. 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]  

  • 20. 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]  

    [Next]    [New Search]
    of 7.