LVP extraction and triplet-based segmentation for diabetic retinopathy recognition
暂无分享,去创建一个
[1] Satish R. Todmal,et al. Eyelids, Eyelashes Detection Algorithm and Hough Transform Method for Noise Removal in Iris Recognition , 2015 .
[2] Bálint Antal,et al. Improving microaneurysm detection using an optimally selected subset of candidate extractors and preprocessing methods , 2012, Pattern Recognit..
[3] Jaskirat Kaur,et al. A generalized method for the segmentation of exudates from pathological retinal fundus images , 2018 .
[4] B. Bala Krishna,et al. Impact of tamanu oil-diesel blend on combustion, performance and emissions of diesel engine and its prediction methodology , 2017 .
[5] Jamshid Dehmeshki,et al. Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy , 2015, Comput. Medical Imaging Graph..
[6] Raghunath S. Holambe,et al. Half-Iris Feature Extraction and Recognition Using a New Class of Biorthogonal Triplet Half-Band Filter Bank and Flexible k-out-of-n:A Postclassifier , 2012, IEEE Transactions on Information Forensics and Security.
[7] T. S. Subashini,et al. Detection of Heart Muscle Damage from Automated Analysis of Echocardiogram Video , 2015 .
[8] Ravinda G. N. Meegama,et al. Detection of hard exudates from diabetic retinopathy images using fuzzy logic , 2013, IET Image Process..
[9] T. Senthil Murugan,et al. Cluster head selection for energy efficient and delay-less routing in wireless sensor network , 2017, Wireless Networks.
[10] Pascale Massin,et al. A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina , 2002, IEEE Transactions on Medical Imaging.
[11] David Zhang,et al. Detecting Diabetes Mellitus and Nonproliferative Diabetic Retinopathy Using Tongue Color, Texture, and Geometry Features , 2014, IEEE Transactions on Biomedical Engineering.
[12] Jayanthi Sivaswamy,et al. A Successive Clutter-Rejection-Based Approach for Early Detection of Diabetic Retinopathy , 2011, IEEE Transactions on Biomedical Engineering.
[13] Geir Joner,et al. Mutations in the Insulin Gene Can Cause MODY and Autoantibody-Negative Type 1 Diabetes , 2008, Diabetes.
[14] András Hajdu,et al. Retinal Microaneurysm Detection Through Local Rotating Cross-Section Profile Analysis , 2013, IEEE Transactions on Medical Imaging.
[15] Wasi Haider Butt,et al. Retinal Images: Optic Disk Localization and Detection , 2010, ICIAR.
[16] Qin Li,et al. Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs , 2010, IEEE Transactions on Medical Imaging.
[17] Dinesh Kumar,et al. Validating retinal fundus image analysis algorithms: issues and a proposal. , 2013, Investigative ophthalmology & visual science.
[18] Shital S. Bramhe,et al. Glass Shaped Antenna with Defected Ground Structure for Cognitive Radio Application , 2015, 2015 International Conference on Computing Communication Control and Automation.
[19] Bram van Ginneken,et al. Information Fusion for Diabetic Retinopathy CAD in Digital Color Fundus Photographs , 2009, IEEE Transactions on Medical Imaging.
[20] Farida Cheriet,et al. Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening , 2016, IEEE Transactions on Medical Imaging.
[21] S. S. Chee,et al. Artificial neural network for classification of depressive and normal in EEG , 2016, 2016 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES).
[22] Subrahmanyam Murala,et al. Edgy salient local binary patterns in inter-plane relationship for image retrieval in Diabetic Retinopathy , 2017 .
[23] M. Usman Akram,et al. Automated Detection of Dark and Bright Lesions in Retinal Images for Early Detection of Diabetic Retinopathy , 2012, Journal of Medical Systems.
[24] U. Rajendra Acharya,et al. Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: A hybrid feature extraction approach , 2013, Knowl. Based Syst..
[25] Devvi Sarwinda,et al. Fundus image texture features analysis in diabetic retinopathy diagnosis , 2017, 2017 Eleventh International Conference on Sensing Technology (ICST).
[26] Guy Cazuguel,et al. FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE , 2014 .
[27] Bram van Ginneken,et al. Segmentation of the Optic Disc, Macula and Vascular Arch in Fundus Photographs , 2007, IEEE Transactions on Medical Imaging.
[28] Alireza Osareh,et al. A Computational-Intelligence-Based Approach for Detection of Exudates in Diabetic Retinopathy Images , 2009, IEEE Transactions on Information Technology in Biomedicine.
[29] Elli Angelopoulou,et al. Retinal vessel segmentation by improved matched filtering: evaluation on a new high-resolution fundus image database , 2013, IET Image Process..
[30] J.-F. Motsch,et al. Modelling and recognition of brainstem auditory evoked potentials using Symlet wavelet , 2000 .
[31] Gwénolé Quellec,et al. Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs , 2008, IEEE Transactions on Medical Imaging.
[32] Kavita Bhatnagar,et al. Extending the Neural Model to Study the Impact of Effective Area of Optical Fiber on Laser Intensity , 2017 .
[33] Muhammad Sharif,et al. A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions , 2017, J. Comput. Sci..
[34] Kjersti Engan,et al. Retinal Disease Screening Through Local Binary Patterns , 2017, IEEE Journal of Biomedical and Health Informatics.
[35] Xiangchu Feng,et al. Variational and PCA based natural image segmentation , 2013, Pattern Recognit..
[36] Xiaohui Liu,et al. Segmentation of the Blood Vessels and Optic Disk in Retinal Images , 2014, IEEE Journal of Biomedical and Health Informatics.
[37] Marios S. Pattichis,et al. Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection , 2010, IEEE Transactions on Medical Imaging.
[38] Ninu Preetha Nirmala Sreedharan,et al. Grey Wolf optimisation-based feature selection and classification for facial emotion recognition , 2018, IET Biom..
[39] V. Sugumaran,et al. A comparative study of Naïve Bayes classifier and Bayes net classifier for fault diagnosis of monoblock centrifugal pump using wavelet analysis , 2012, Appl. Soft Comput..
[40] Venu Govindaraju,et al. Improved k-nearest neighbor classification , 2002, Pattern Recognit..
[41] Meindert Niemeijer,et al. Evaluation of a computer-aided diagnosis system for diabetic retinopathy screening on public data. , 2011, Investigative ophthalmology & visual science.
[42] J. Lina,et al. Complex Daubechies Wavelets , 1995 .
[43] B. Klein,et al. Global Prevalence and Major Risk Factors of Diabetic Retinopathy , 2012, Diabetes Care.
[44] Anastasios N. Venetsanopoulos,et al. Biorthogonal nearly coiflet wavelets for image compression , 2001, Signal Process. Image Commun..
[45] Bunyarit Uyyanonvara,et al. Simple hybrid method for fine microaneurysm detection from non-dilated diabetic retinopathy retinal images , 2013, Comput. Medical Imaging Graph..
[46] Gwénolé Quellec,et al. A multiple-instance learning framework for diabetic retinopathy screening , 2012, Medical Image Anal..
[47] Qin Li,et al. Detection of microaneurysms using multi-scale correlation coefficients , 2010, Pattern Recognit..
[48] M Iyapparaja,et al. Security policy speculation of user uploaded images on content sharing sites , 2017 .
[49] Kurt Hornik,et al. The support vector machine under test , 2003, Neurocomputing.
[50] Randeep Kaur,et al. Comparison of contrast enhancement techniques for medical image , 2016, 2016 Conference on Emerging Devices and Smart Systems (ICEDSS).
[51] G. Sasibhushana Rao,et al. Performance Analysis of Orthogonal and Biorthogonal Wavelets for Edge Detection of X-ray Images , 2016 .
[52] Ana Maria Mendonça,et al. Automatic localization of the optic disc by combining vascular and intensity information , 2013, Comput. Medical Imaging Graph..
[53] A. S. Manjunath,et al. Improved entropy encoding for high efficient video coding standard , 2016 .
[54] Alhadi Bustamam,et al. Classification of diabetic retinopathy through texture features analysis , 2017, 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS).
[55] Gwénolé Quellec,et al. Exudate detection in color retinal images for mass screening of diabetic retinopathy , 2014, Medical Image Anal..