BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

186 related articles for article (PubMed ID: 26469792)

  • 1. Retinal Disease Screening Through Local Binary Patterns.
    Morales S; Engan K; Naranjo V; Colomer A
    IEEE J Biomed Health Inform; 2017 Jan; 21(1):184-192. PubMed ID: 26469792
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Retinal image analysis for disease screening through local tetra patterns.
    Porwal P; Pachade S; Kokare M; Giancardo L; Mériaudeau F
    Comput Biol Med; 2018 Nov; 102():200-210. PubMed ID: 30308336
    [TBL] [Abstract][Full Text] [Related]  

  • 3. 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]  

  • 4. A supervised joint multi-layer segmentation framework for retinal optical coherence tomography images using conditional random field.
    Chakravarty A; Sivaswamy J
    Comput Methods Programs Biomed; 2018 Oct; 165():235-250. PubMed ID: 30337078
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Automated screening system for retinal health using bi-dimensional empirical mode decomposition and integrated index.
    Acharya UR; Mookiah MR; Koh JE; Tan JH; Bhandary SV; Rao AK; Fujita H; Hagiwara Y; Chua CK; Laude A
    Comput Biol Med; 2016 Aug; 75():54-62. PubMed ID: 27253617
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images.
    Mookiah MR; Acharya UR; Koh JE; Chandran V; Chua CK; Tan JH; Lim CM; Ng EY; Noronha K; Tong L; Laude A
    Comput Biol Med; 2014 Oct; 53():55-64. PubMed ID: 25127409
    [TBL] [Abstract][Full Text] [Related]  

  • 7. 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]  

  • 8. A tool for automated diabetic retinopathy pre-screening based on retinal image computer analysis.
    Gegundez-Arias ME; Marin D; Ponte B; Alvarez F; Garrido J; Ortega C; Vasallo MJ; Bravo JM
    Comput Biol Med; 2017 Sep; 88():100-109. PubMed ID: 28711766
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Recent trends and advances in fundus image analysis: A review.
    Iqbal S; Khan TM; Naveed K; Naqvi SS; Nawaz SJ
    Comput Biol Med; 2022 Dec; 151(Pt A):106277. PubMed ID: 36370579
    [TBL] [Abstract][Full Text] [Related]  

  • 10. A Machine Learning Ensemble Classifier for Early Prediction of Diabetic Retinopathy.
    S K S; P A
    J Med Syst; 2017 Nov; 41(12):201. PubMed ID: 29124453
    [TBL] [Abstract][Full Text] [Related]  

  • 11. 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]  

  • 12. Deep image mining for diabetic retinopathy screening.
    Quellec G; Charrière K; Boudi Y; Cochener B; Lamard M
    Med Image Anal; 2017 Jul; 39():178-193. PubMed ID: 28511066
    [TBL] [Abstract][Full Text] [Related]  

  • 13. An Automated System for the Detection and Classification of Retinal Changes Due to Red Lesions in Longitudinal Fundus Images.
    Adal KM; van Etten PG; Martinez JP; Rouwen KW; Vermeer KA; van Vliet LJ
    IEEE Trans Biomed Eng; 2018 Jun; 65(6):1382-1390. PubMed ID: 28922110
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Automatic Detection of Optic Disc in Retinal Image by Using Keypoint Detection, Texture Analysis, and Visual Dictionary Techniques.
    Akyol K; Şen B; Bayır Ş
    Comput Math Methods Med; 2016; 2016():6814791. PubMed ID: 27110272
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies.
    Koh JEW; Acharya UR; Hagiwara Y; Raghavendra U; Tan JH; Sree SV; Bhandary SV; Rao AK; Sivaprasad S; Chua KC; Laude A; Tong L
    Comput Biol Med; 2017 May; 84():89-97. PubMed ID: 28351716
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Detection of Early Signs of Diabetic Retinopathy Based on Textural and Morphological Information in Fundus Images.
    Colomer A; Igual J; Naranjo V
    Sensors (Basel); 2020 Feb; 20(4):. PubMed ID: 32069912
    [TBL] [Abstract][Full Text] [Related]  

  • 17. A contribution of image processing to the diagnosis of diabetic retinopathy--detection of exudates in color fundus images of the human retina.
    Walter T; Klein JC; Massin P; Erginay A
    IEEE Trans Med Imaging; 2002 Oct; 21(10):1236-43. PubMed ID: 12585705
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening.
    Seoud L; Hurtut T; Chelbi J; Cheriet F; Langlois JM
    IEEE Trans Med Imaging; 2016 Apr; 35(4):1116-26. PubMed ID: 26701180
    [TBL] [Abstract][Full Text] [Related]  

  • 19. A computer-aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images.
    ElTanboly A; Ismail M; Shalaby A; Switala A; El-Baz A; Schaal S; Gimel'farb G; El-Azab M
    Med Phys; 2017 Mar; 44(3):914-923. PubMed ID: 28035657
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Automated retinal health diagnosis using pyramid histogram of visual words and Fisher vector techniques.
    Koh JEW; Ng EYK; Bhandary SV; Hagiwara Y; Laude A; Acharya UR
    Comput Biol Med; 2018 Jan; 92():204-209. PubMed ID: 29227822
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 10.