121 related articles for article (PubMed ID: 17945839)
1. 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]
2. 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]
3. 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]
4. 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]
5. Automated detection and quantification of retinal exudates.
Phillips R; Forrester J; Sharp P
Graefes Arch Clin Exp Ophthalmol; 1993 Feb; 231(2):90-4. PubMed ID: 8444365
[TBL] [Abstract][Full Text] [Related]
6. 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]
7. 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]
8. 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]
9. Automatic Detection of Hard Exudates in Color Retinal Images Using Dynamic Threshold and SVM Classification: Algorithm Development and Evaluation.
Long S; Huang X; Chen Z; Pardhan S; Zheng D
Biomed Res Int; 2019; 2019():3926930. PubMed ID: 30809539
[TBL] [Abstract][Full Text] [Related]
10. 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]
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. 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 diabetic retinopathy on digital fundus images.
Sinthanayothin C; Boyce JF; Williamson TH; Cook HL; Mensah E; Lal S; Usher D
Diabet Med; 2002 Feb; 19(2):105-12. PubMed ID: 11874425
[TBL] [Abstract][Full Text] [Related]
14. Exudate detection in color retinal images for mass screening of diabetic retinopathy.
Zhang X; Thibault G; Decencière E; Marcotegui B; Laÿ B; Danno R; Cazuguel G; Quellec G; Lamard M; Massin P; Chabouis A; Victor Z; Erginay A
Med Image Anal; 2014 Oct; 18(7):1026-43. PubMed ID: 24972380
[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. 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]
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. [Retinal image analysis to detect lesions associated with diabetic retinopathy].
Sánchez Gutiérrez CI; López Gálvez MI; Hornero Sánchez R; Poza Crespo J
Arch Soc Esp Oftalmol; 2004 Dec; 79(12):623-8. PubMed ID: 15627932
[TBL] [Abstract][Full Text] [Related]
19. An exudate detection method for diagnosis risk of diabetic macular edema in retinal images using feature-based and supervised classification.
Marin D; Gegundez-Arias ME; Ponte B; Alvarez F; Garrido J; Ortega C; Vasallo MJ; Bravo JM
Med Biol Eng Comput; 2018 Aug; 56(8):1379-1390. PubMed ID: 29318442
[TBL] [Abstract][Full Text] [Related]
20. Decision support system for the detection and grading of hard exudates from color fundus photographs.
Jaafar HF; Nandi AK; Al-Nuaimy W
J Biomed Opt; 2011 Nov; 16(11):116001. PubMed ID: 22112106
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]