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.
154 related articles for article (PubMed ID: 22255692)
1. Microaneurysm detection with radon transform-based classification on retina images. Giancardo L; Meriaudeau F; Karnowski TP; Li Y; Tobin KW; Chaum E Annu Int Conf IEEE Eng Med Biol Soc; 2011; 2011():5939-42. PubMed ID: 22255692 [TBL] [Abstract][Full Text] [Related]
2. Detection of lesions in retina photographs based on the wavelet transform. Quellec G; Lamard M; Josselin PM; Cazuguel G; Cochener B; Roux C Conf Proc IEEE Eng Med Biol Soc; 2006; 2006():2618-21. PubMed ID: 17945729 [TBL] [Abstract][Full Text] [Related]
3. 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]
4. Evaluation of the grading performance of an ensemble-based microaneurysm detector. Antal B; Lázár I; Hajdu A; Török Z; Csutak A; Peto T Annu Int Conf IEEE Eng Med Biol Soc; 2011; 2011():5943-6. PubMed ID: 22255693 [TBL] [Abstract][Full Text] [Related]
5. Retina lesion and microaneurysm segmentation using morphological reconstruction methods with ground-truth data. Karnowski TP; Govindasamy V; Tobin KW; Chaum E; Abramoff MD Annu Int Conf IEEE Eng Med Biol Soc; 2008; 2008():5433-6. PubMed ID: 19163946 [TBL] [Abstract][Full Text] [Related]
6. Optimal wavelet transform for the detection of microaneurysms in retina photographs. Quellec G; Lamard M; Josselin PM; Cazuguel G; Cochener B; Roux C IEEE Trans Med Imaging; 2008 Sep; 27(9):1230-41. PubMed ID: 18779064 [TBL] [Abstract][Full Text] [Related]
7. 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]
8. Optimal algorithm for automatic detection of microaneurysms based on receiver operating characteristic curve. Xu L; Luo S J Biomed Opt; 2010; 15(6):065004. PubMed ID: 21198168 [TBL] [Abstract][Full Text] [Related]
9. Automated microaneurysm detection using local contrast normalization and local vessel detection. Fleming AD; Philip S; Goatman KA; Olson JA; Sharp PF IEEE Trans Med Imaging; 2006 Sep; 25(9):1223-32. PubMed ID: 16967807 [TBL] [Abstract][Full Text] [Related]
10. Automatic detection of retina disease: robustness to image quality and localization of anatomy structure. Karnowski TP; Aykac D; Giancardo L; Li Y; Nichols T; Tobin KW; Chaum E Annu Int Conf IEEE Eng Med Biol Soc; 2011; 2011():5959-64. PubMed ID: 22255697 [TBL] [Abstract][Full Text] [Related]
11. Algorithms for digital image processing in diabetic retinopathy. Winder RJ; Morrow PJ; McRitchie IN; Bailie JR; Hart PM Comput Med Imaging Graph; 2009 Dec; 33(8):608-22. PubMed ID: 19616920 [TBL] [Abstract][Full Text] [Related]
12. Retinal microaneurysm detection through local rotating cross-section profile analysis. Lazar I; Hajdu A IEEE Trans Med Imaging; 2013 Feb; 32(2):400-7. PubMed ID: 23192523 [TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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]
15. 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]
16. Machine learning and pattern classification in identification of indigenous retinal pathology. Jelinek HF; Rocha A; Carvalho T; Goldenstein S; Wainer J Annu Int Conf IEEE Eng Med Biol Soc; 2011; 2011():5951-4. PubMed ID: 22255695 [TBL] [Abstract][Full Text] [Related]
17. 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]
18. 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]
19. Patch-based automatic retinal vessel segmentation in global and local structural context. Cao S; Bharath AA; Parker KH; Ng J Annu Int Conf IEEE Eng Med Biol Soc; 2012; 2012():4942-5. PubMed ID: 23367036 [TBL] [Abstract][Full Text] [Related]
20. 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] [Next] [New Search]