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 *

167 related articles for article (PubMed ID: 28268569)

  • 21. A review on computer-aided recent developments for automatic detection of diabetic retinopathy.
    Randive SN; Senapati RK; Rahulkar AD
    J Med Eng Technol; 2019 Feb; 43(2):87-99. PubMed ID: 31198073
    [TBL] [Abstract][Full Text] [Related]  

  • 22. Diagnosis of diabetic retinopathy: automatic extraction of optic disc and exudates from retinal images using marker-controlled watershed transformation.
    Reza AW; Eswaran C; Dimyati K
    J Med Syst; 2011 Dec; 35(6):1491-501. PubMed ID: 20703768
    [TBL] [Abstract][Full Text] [Related]  

  • 23. A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images.
    Liu Q; Zou B; Chen J; Ke W; Yue K; Chen Z; Zhao G
    Comput Med Imaging Graph; 2017 Jan; 55():78-86. PubMed ID: 27665058
    [TBL] [Abstract][Full Text] [Related]  

  • 24. Detection of new vessels on the optic disc using retinal photographs.
    Goatman KA; Fleming AD; Philip S; Williams GJ; Olson JA; Sharp PF
    IEEE Trans Med Imaging; 2011 Apr; 30(4):972-9. PubMed ID: 21156389
    [TBL] [Abstract][Full Text] [Related]  

  • 25. Automatic optic disk detection in retinal images using hybrid vessel phase portrait analysis.
    Muangnak N; Aimmanee P; Makhanov S
    Med Biol Eng Comput; 2018 Apr; 56(4):583-598. PubMed ID: 28836125
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Detection of neovascularization in diabetic retinopathy.
    Hassan SS; Bong DB; Premsenthil M
    J Digit Imaging; 2012 Jun; 25(3):437-44. PubMed ID: 21901535
    [TBL] [Abstract][Full Text] [Related]  

  • 27. 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]  

  • 28. The fractal geometry of proliferative diabetic retinopathy: implications for the diagnosis and the process of retinal vasculogenesis.
    Daxer A
    Curr Eye Res; 1993 Dec; 12(12):1103-9. PubMed ID: 8137633
    [TBL] [Abstract][Full Text] [Related]  

  • 29. 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]  

  • 30. Weakly Supervised Sensitive Heatmap framework to classify and localize diabetic retinopathy lesions.
    Al-Mukhtar M; Morad AH; Albadri M; Islam MDS
    Sci Rep; 2021 Dec; 11(1):23631. PubMed ID: 34880311
    [TBL] [Abstract][Full Text] [Related]  

  • 31. Detection of neovascularization based on fractal and texture analysis with interaction effects in diabetic retinopathy.
    Lee J; Zee BC; Li Q
    PLoS One; 2013; 8(12):e75699. PubMed ID: 24358105
    [TBL] [Abstract][Full Text] [Related]  

  • 32. Optic disc detection in retinal fundus images using gravitational law-based edge detection.
    Alshayeji M; Al-Roomi SA; Abed S
    Med Biol Eng Comput; 2017 Jun; 55(6):935-948. PubMed ID: 27638111
    [TBL] [Abstract][Full Text] [Related]  

  • 33. Segmentation of the blood vessels and optic disk in retinal images.
    Salazar-Gonzalez A; Kaba D; Li Y; Liu X
    IEEE J Biomed Health Inform; 2014 Nov; 18(6):1874-86. PubMed ID: 25265617
    [TBL] [Abstract][Full Text] [Related]  

  • 34. An integrated index for the identification of diabetic retinopathy stages using texture parameters.
    Acharya UR; Ng EY; Tan JH; Sree SV; Ng KH
    J Med Syst; 2012 Jun; 36(3):2011-20. PubMed ID: 21340703
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Weighted ensemble based automatic detection of exudates in fundus photographs.
    Prentasic P; Loncaric S
    Annu Int Conf IEEE Eng Med Biol Soc; 2014; 2014():138-41. PubMed ID: 25569916
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Automated detection of optic disk in retinal fundus images using intuitionistic fuzzy histon segmentation.
    Mookiah MR; Acharya UR; Chua CK; Min LC; Ng EY; Mushrif MM; Laude A
    Proc Inst Mech Eng H; 2013 Jan; 227(1):37-49. PubMed ID: 23516954
    [TBL] [Abstract][Full Text] [Related]  

  • 37. Feature Selection and Parameters Optimization of Support Vector Machines Based on Hybrid Glowworm Swarm Optimization for Classification of Diabetic Retinopathy.
    Karthikeyan R; Alli P
    J Med Syst; 2018 Sep; 42(10):195. PubMed ID: 30209620
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Learning-based approach for the automatic detection of the optic disc in digital retinal fundus photographs.
    Wong DK; Liu J; Tan NM; Yin F; Lee BH; Wong TY
    Annu Int Conf IEEE Eng Med Biol Soc; 2010; 2010():5355-8. PubMed ID: 21096259
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Automated classification of diabetic retinopathy through reliable feature selection.
    Gayathri S; Gopi VP; Palanisamy P
    Phys Eng Sci Med; 2020 Sep; 43(3):927-945. PubMed ID: 32648111
    [TBL] [Abstract][Full Text] [Related]  

  • 40. Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features.
    Abbas Q; Fondon I; Sarmiento A; Jiménez S; Alemany P
    Med Biol Eng Comput; 2017 Nov; 55(11):1959-1974. PubMed ID: 28353133
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 9.