Small sample color fundus image quality assessment based on gcforest

[1]  Anil Kumar Tiwari,et al.  Multivariate Regression-Based Convolutional Neural Network Model for Fundus Image Quality Assessment , 2020, IEEE Access.

[2]  Ali Serener,et al.  A Generalized Deep Learning Model for Glaucoma Detection , 2019, 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT).

[3]  Huazhu Fu,et al.  Evaluation of Retinal Image Quality Assessment Networks in Different Color-spaces , 2019, MICCAI.

[4]  Anil Kumar Tiwari,et al.  Fundus image quality assessment: survey, challenges, and future scope , 2019, IET Image Process..

[5]  Renoh Johnson Chalakkal,et al.  Quality and content analysis of fundus images using deep learning , 2019, Comput. Biol. Medicine.

[6]  Subhashini Venugopalan,et al.  Detection of anaemia from retinal fundus images via deep learning , 2019, Nature Biomedical Engineering.

[7]  Valery Naranjo,et al.  CNNs for automatic glaucoma assessment using fundus images: an extensive validation , 2019, BioMedical Engineering OnLine.

[8]  Jeong-Hoi Koo,et al.  A compact pulsatile simulator based on cam-follower mechanism for generating radial pulse waveforms , 2019, BioMedical Engineering OnLine.

[9]  Bernadette Dorizzi,et al.  Retinal image quality assessment using deep learning , 2018, Comput. Biol. Medicine.

[10]  Donald G. Dansereau,et al.  Glare-free retinal imaging using a portable light field fundus camera , 2018, Biomedical optics express.

[11]  Sajib Saha,et al.  Automated Quality Assessment of Colour Fundus Images for Diabetic Retinopathy Screening in Telemedicine , 2018, Journal of Digital Imaging.

[12]  Ahmed El-Rafei,et al.  No-reference quality index for color retinal images , 2017, Comput. Biol. Medicine.

[13]  Sajib Saha,et al.  Deep Learning for Automated Quality Assessment of Color Fundus Images in Diabetic Retinopathy Screening , 2017, ArXiv.

[14]  Ji Feng,et al.  Deep forest , 2017, IJCAI.

[15]  Sajib Saha,et al.  A Two-Step Approach for Longitudinal Registration of Retinal Images , 2016, Journal of Medical Systems.

[16]  Dahong Qian,et al.  Human Visual System-Based Fundus Image Quality Assessment of Portable Fundus Camera Photographs , 2016, IEEE Transactions on Medical Imaging.

[17]  D. Strachan,et al.  Automated retinal image quality assessment on the UK Biobank dataset for epidemiological studies , 2016, Comput. Biol. Medicine.

[18]  Kai Zeng,et al.  Quality Prediction of Asymmetrically Distorted Stereoscopic 3D Images , 2015, IEEE Transactions on Image Processing.

[19]  Kai Zeng,et al.  Display device-adapted video quality-of-experience assessment , 2015, Electronic Imaging.

[20]  Yi Zhu,et al.  Towards a comprehensive model for predicting the quality of individual visual experience , 2015, Electronic Imaging.

[21]  Luís Alberto da Silva Cruz,et al.  Retinal image quality assessment using generic image quality indicators , 2014, Inf. Fusion.

[22]  Hidayet Erdöl,et al.  Identification of suitable fundus images using automated quality assessment methods , 2014, Journal of biomedical optics.

[23]  Farida Cheriet,et al.  Retinal image quality assessment using generic features , 2014, Medical Imaging.

[24]  Martin Kraus,et al.  Automatic no-reference quality assessment for retinal fundus images using vessel segmentation , 2013, Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems.

[25]  Alan C. Bovik,et al.  No-Reference Image Quality Assessment in the Spatial Domain , 2012, IEEE Transactions on Image Processing.

[26]  J. Olson,et al.  Automated clarity assessment of retinal images using regionally based structural and statistical measures. , 2012, Medical engineering & physics.

[27]  Tien Yin Wong,et al.  Diabetic retinopathy , 2010, The Lancet.

[28]  I. Deary,et al.  Retinal image analysis: Concepts, applications and potential , 2006, Progress in Retinal and Eye Research.

[29]  G Zahlmann,et al.  Feasibility of using the TOSCA telescreening procedures for diabetic retinopathy , 2004, Diabetic medicine : a journal of the British Diabetic Association.

[30]  Zhou Wang,et al.  Multiscale structural similarity for image quality assessment , 2003, The Thrity-Seventh Asilomar Conference on Signals, Systems & Computers, 2003.

[31]  Yiming Wang,et al.  Automatic retinal image quality assessment and enhancement , 1999, Medical Imaging.

[32]  D. Ruderman,et al.  Statistics of cone responses to natural images: implications for visual coding , 1998 .

[33]  丛 婧 Cong Jing,et al.  Smartphone-based fundus imaging system , 2019 .

[34]  Gangyi Jiang,et al.  Automated Quality Assessment of Fundus Images via Analysis of Illumination, Naturalness and Structure , 2018, IEEE Access.

[35]  范赐恩 Fan Ci-en,et al.  No-reference image quality assessment using joint color space statistical and texture feature , 2018 .

[36]  Kenneth W. Tobin,et al.  Quality Assessment of Retinal Fundus Images using Elliptical Local Vessel Density , 2010 .

[37]  Joan W. Miller,et al.  Age-related macular degeneration. , 2008, The New England journal of medicine.

[38]  A. Ramé.  [Age-related macular degeneration]. , 2006, Revue de l'infirmiere.

[39]  T. Sano,et al.  [Diabetic retinopathy]. , 2001, Nihon rinsho. Japanese journal of clinical medicine.

[40]  M. Ogiela.  Multimedia tools and applications , 1995 .

[41]  J. Albers,et al.  Interaction of Color , 1971 .