214 related articles for article (PubMed ID: 25863517)
1. 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]
2. An automated retinal imaging method for the early diagnosis of diabetic retinopathy.
Franklin SW; Rajan SE
Technol Health Care; 2013; 21(6):557-69. PubMed ID: 24284549
[TBL] [Abstract][Full Text] [Related]
3. Retinal image assessment using bi-level adaptive morphological component analysis.
Javidi M; Harati A; Pourreza H
Artif Intell Med; 2019 Aug; 99():101702. PubMed ID: 31606110
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Automated detection of exudates for diabetic retinopathy screening.
Fleming AD; Philip S; Goatman KA; Williams GJ; Olson JA; Sharp PF
Phys Med Biol; 2007 Dec; 52(24):7385-96. PubMed ID: 18065845
[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. 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]
8. A multiple-instance learning framework for diabetic retinopathy screening.
Quellec G; Lamard M; Abràmoff MD; Decencière E; Lay B; Erginay A; Cochener B; Cazuguel G
Med Image Anal; 2012 Aug; 16(6):1228-40. PubMed ID: 22850462
[TBL] [Abstract][Full Text] [Related]
9. Automated Identification of Diabetic Retinopathy Using Deep Learning.
Gargeya R; Leng T
Ophthalmology; 2017 Jul; 124(7):962-969. PubMed ID: 28359545
[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. Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy.
Welikala RA; Fraz MM; Dehmeshki J; Hoppe A; Tah V; Mann S; Williamson TH; Barman SA
Comput Med Imaging Graph; 2015 Jul; 43():64-77. PubMed ID: 25841182
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. 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]
14. 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]
15. 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]
16. 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]
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. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.
Gulshan V; Peng L; Coram M; Stumpe MC; Wu D; Narayanaswamy A; Venugopalan S; Widner K; Madams T; Cuadros J; Kim R; Raman R; Nelson PC; Mega JL; Webster DR
JAMA; 2016 Dec; 316(22):2402-2410. PubMed ID: 27898976
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Improvement of retinal blood vessel detection using morphological component analysis.
Imani E; Javidi M; Pourreza HR
Comput Methods Programs Biomed; 2015 Mar; 118(3):263-79. PubMed ID: 25697986
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
