551 related articles for article (PubMed ID: 26378502)
1. 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]
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. 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]
5. Evaluation of automated fundus photograph analysis algorithms for detecting microaneurysms, haemorrhages and exudates, and of a computer-assisted diagnostic system for grading diabetic retinopathy.
Dupas B; Walter T; Erginay A; Ordonez R; Deb-Joardar N; Gain P; Klein JC; Massin P
Diabetes Metab; 2010 Jun; 36(3):213-20. PubMed ID: 20219404
[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. 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]
8. An ensemble-based system for microaneurysm detection and diabetic retinopathy grading.
Antal B; Hajdu A
IEEE Trans Biomed Eng; 2012 Jun; 59(6):1720-6. PubMed ID: 22481810
[TBL] [Abstract][Full Text] [Related]
9. Automated detection of red lesions from digital colour fundus photographs.
Jaafar HF; Nandi AK; Al-Nuaimy W
Annu Int Conf IEEE Eng Med Biol Soc; 2011; 2011():6232-5. PubMed ID: 22255763
[TBL] [Abstract][Full Text] [Related]
10. 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]
11. DREAM: diabetic retinopathy analysis using machine learning.
Roychowdhury S; Koozekanani DD; Parhi KK
IEEE J Biomed Health Inform; 2014 Sep; 18(5):1717-28. PubMed ID: 25192577
[TBL] [Abstract][Full Text] [Related]
12. 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]
13. 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]
14. Discrimination of retinal images containing bright lesions using sparse coded features and SVM.
Sidibé D; Sadek I; Mériaudeau F
Comput Biol Med; 2015 Jul; 62():175-84. PubMed ID: 25935125
[TBL] [Abstract][Full Text] [Related]
15. Automatic detection of blood vessels in retinal images for diabetic retinopathy diagnosis.
Raja DS; Vasuki S
Comput Math Methods Med; 2015; 2015():419279. PubMed ID: 25810749
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Automated Diabetic Retinopathy Screening and Monitoring Using Retinal Fundus Image Analysis.
Bhaskaranand M; Ramachandra C; Bhat S; Cuadros J; Nittala MG; Sadda S; Solanki K
J Diabetes Sci Technol; 2016 Feb; 10(2):254-61. PubMed ID: 26888972
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. 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]
20. Mathematical morphology for microaneurysm detection in fundus images.
Joshi S; Karule PT
Eur J Ophthalmol; 2020 Sep; 30(5):1135-1142. PubMed ID: 31018679
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]