Classifiers fusion for improved vessel recognition with application in quantification of generalized arteriolar narrowing

This paper attempts to estimate diagnostically relevant measure, i.e., Arteriovenous Ratio with an improved retinal vessel classification using feature ranking strategies and multiple classifiers d...

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

[2]  Michael D. Abràmoff,et al.  An improved arteriovenous classification method for the early diagnostics of various diseases in retinal image , 2017, Comput. Methods Programs Biomed..

[3]  R. Klein,et al.  Revised formulas for summarizing retinal vessel diameters , 2003, Current eye research.

[4]  George Azzopardi,et al.  Automatic detection of vascular bifurcations in segmented retinal images using trainable COSFIRE filters , 2013, Pattern Recognit. Lett..

[5]  Julian Quiroga,et al.  Support system for the preventive diagnosis of Hypertensive Retinopathy , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[6]  Emanuele Trucco,et al.  Retinal Vessel Classification Based on Maximization of Squared-Loss Mutual Information , 2016 .

[7]  Yanchun Zhang,et al.  Accurate vessel segmentation using maximum entropy incorporating line detection and phase-preserving denoising , 2017, Comput. Vis. Image Underst..

[8]  Igor Kononenko,et al.  Estimating Attributes: Analysis and Extensions of RELIEF , 1994, ECML.

[9]  Cordelia Schmid,et al.  Combining attributes and Fisher vectors for efficient image retrieval , 2011, CVPR 2011.

[10]  Ana Maria Mendonça,et al.  Automatic Classification of Retinal Vessels Using Structural and Intensity Information , 2013, IbPRIA.

[11]  M. Tso,et al.  Pathophysiology of hypertensive retinopathy. , 1982, Ophthalmology.

[12]  David G. Kirkpatrick,et al.  Linear Time Euclidean Distance Algorithms , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Behdad Dashtbozorg,et al.  An automatic method for the estimation of Arteriolar-to-Venular Ratio in retinal images , 2013, Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems.

[14]  Jiri Matas,et al.  On Combining Classifiers , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[15]  Sašo Džeroski,et al.  Improved medical image modality classification using a combination of visual and textual features , 2015, Comput. Medical Imaging Graph..

[16]  H G SCHEIE,et al.  Evaluation of ophthalmoscopic changes of hypertension and arteriolar sclerosis. , 1953, A.M.A. archives of ophthalmology.

[17]  Farshad Tajeripour,et al.  Computerized Medical Imaging and Graphics Automated Characterization of Blood Vessels as Arteries and Veins in Retinal Images , 2022 .

[18]  Manuel G. Penedo,et al.  On the Automatic Computation of the Arterio-Venous Ratio in Retinal Images: Using Minimal Paths for the Artery/Vein Classification , 2010, 2010 International Conference on Digital Image Computing: Techniques and Applications.

[19]  Valérie Biousse,et al.  Nonmydriatic ocular fundus photography in the emergency department. , 2011, The New England journal of medicine.

[20]  Miguel Ángel Guevara-López,et al.  Improving the Mann-Whitney statistical test for feature selection: An approach in breast cancer diagnosis on mammography , 2015, Artif. Intell. Medicine.

[21]  Bram van Ginneken,et al.  Automated Measurement of the Arteriolar-to-Venular Width Ratio in Digital Color Fundus Photographs , 2011, IEEE Transactions on Medical Imaging.

[22]  Xiaoyi Jiang,et al.  Separation of the retinal vascular graph in arteries and veins based upon structural knowledge , 2009, Image Vis. Comput..

[23]  Joost van de Weijer,et al.  Fast Anisotropic Gauss Filtering , 2002, ECCV.

[24]  M. Usman Akram,et al.  A Robust Algorithm for Optic Disc Segmentation from Colored Fundus Images , 2014, ICIAR.

[25]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[26]  Xiaoxia Yin,et al.  Accurate Image Analysis of the Retina Using Hessian Matrix and Binarisation of Thresholded Entropy with Application of Texture Mapping , 2014, PloS one.

[27]  Chih-Fong Tsai,et al.  SVM and SVM Ensembles in Breast Cancer Prediction , 2017, PloS one.

[28]  Manuel G. Penedo,et al.  Improving retinal artery and vein classification by means of a minimal path approach , 2012, Machine Vision and Applications.

[29]  Jun Zhang,et al.  An automated computational framework for retinal vascular network labeling and branching order analysis. , 2012, Microvascular research.

[30]  Ana Maria Mendonça,et al.  An Automatic Graph-Based Approach for Artery/Vein Classification in Retinal Images , 2014, IEEE Transactions on Image Processing.

[31]  Emanuele Trucco,et al.  Automatic retinal vessel classification using a Least Square-Support Vector Machine in VAMPIRE , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[32]  Tianfu Wang,et al.  A Cross-Modality Learning Approach for Vessel Segmentation in Retinal Images , 2016, IEEE Transactions on Medical Imaging.

[33]  Daoqiang Zhang,et al.  Hierarchical fusion of features and classifier decisions for Alzheimer's disease diagnosis , 2014, Human brain mapping.

[34]  M. Usman Akram.  Retinal Image Preprocessing: Background and Noise Segmentation , 2012 .

[35]  Carlo Tomasi,et al.  Retinal Artery-Vein Classification via Topology Estimation , 2015, IEEE Transactions on Medical Imaging.

[36]  Xiaoxia Yin,et al.  Automatic Optic Disk Segmentation in Presence of Disk Blurring , 2016, ISVC.

[37]  Muhammad Moazam Fraz,et al.  Automated retinal vessel recognition and measurements on large datasets , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[38]  N L Stokoe,et al.  Normal retinal vascular pattern. Arteriovenous ratio as a measure of arterial calibre. , 1966, The British journal of ophthalmology.

[39]  M. Akram,et al.  Classification of vessels as arteries verses veins using hybrid features for diagnosis of hypertensive retinopathy , 2016, 2016 IEEE International Conference on Imaging Systems and Techniques (IST).

[40]  T. Wong,et al.  Hypertensive retinopathy revisited: some answers, more questions , 2005, British Journal of Ophthalmology.

[41]  Valérie Biousse,et al.  The Use of Retinal Photography in Nonophthalmic Settings and Its Potential for Neurology , 2012, The neurologist.

[42]  Keshab K. Parhi,et al.  Artery/vein classification of retinal blood vessels using feature selection , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[43]  Tien Yin Wong,et al.  Systemic associations of retinal microvascular signs: a review of recent population‐based studies , 2005, Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians.

[44]  Kevin W. Bowyer,et al.  Combination of Multiple Classifiers Using Local Accuracy Estimates , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[45]  Carla Agurto,et al.  Clinical utilization of automated image analysis software for improving retinal reader's performance , 2016, 2016 IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI).

[46]  Amir Akramin Shafie,et al.  Vascular intersection detection in retina fundus images using a new hybrid approach , 2010, Comput. Biol. Medicine.

[47]  Tai Sing Lee,et al.  Image Representation Using 2D Gabor Wavelets , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[48]  R C Pruett,et al.  Monochromatic ophthalmoscopy and fundus photography. The normal fundus. , 1977, Archives of ophthalmology.

[49]  R. Klein,et al.  Computer-assisted measurement of retinal vessel diameters in the Beaver Dam Eye Study: methodology, correlation between eyes, and effect of refractive errors. , 2004, Ophthalmology.

[50]  A. Hofman,et al.  Are retinal arteriolar or venular diameters associated with markers for cardiovascular disorders? The Rotterdam Study. , 2004, Investigative ophthalmology & visual science.

[51]  R. Klein,et al.  Methods for evaluation of retinal microvascular abnormalities associated with hypertension/sclerosis in the Atherosclerosis Risk in Communities Study. , 1999, Ophthalmology.

[52]  Emanuele Trucco,et al.  Retinal vessel classification: Sorting arteries and veins , 2013, 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[53]  Jitendra Malik,et al.  Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons , 2001, International Journal of Computer Vision.

[54]  Bogdan Gabrys,et al.  Classifier selection for majority voting , 2005, Inf. Fusion.

[55]  Gang Niu,et al.  Multi-agent decision fusion for motor fault diagnosis , 2007 .

[56]  Cordelia Schmid,et al.  Constructing models for content-based image retrieval , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[57]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[58]  Wei Zhang,et al.  Multiple Classifier Combination for Hyperspectral Remote Sensing Image Classification , 2009, MCS.

[59]  X. Zabulis,et al.  An Image Analysis System for the Assessment of Retinal Microcirculation in Hypertension and Its Clinical Evaluation , 2014 .

[60]  Carla Agurto,et al.  Detection of hypertensive retinopathy using vessel measurements and textural features , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[61]  Wen Gao,et al.  Object detection using spatial histogram features , 2006, Image Vis. Comput..

[62]  Quan Pan,et al.  Classifier Fusion With Contextual Reliability Evaluation , 2018, IEEE Transactions on Cybernetics.

[63]  Alfredo Ruggeri,et al.  A divide et impera strategy for automatic classification of retinal vessels into arteries and veins , 2003, Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE Cat. No.03CH37439).

[64]  Hasan Demirel,et al.  Classification of Alzheimer's disease and prediction of mild cognitive impairment-to-Alzheimer's conversion from structural magnetic resource imaging using feature ranking and a genetic algorithm , 2017, Comput. Biol. Medicine.

[65]  Yvan Saeys,et al.  Robust Feature Selection Using Ensemble Feature Selection Techniques , 2008, ECML/PKDD.

[66]  N. M. Keith,et al.  Some different types of essential hypertension: their course and prognosis. , 1939 .

[67]  K. Vijayarekha,et al.  Hypertensive Retinopathy Diagnosis from Fundus Images by Estimation of Avr. , 2012 .

[68]  J. C. Parr,et al.  Mathematic relationships between the width of a retinal artery and the widths of its branches. , 1974, American journal of ophthalmology.

[69]  M. Usman Akram,et al.  Retinal Blood Vessels Differentiation for Calculation of Arterio-Venous Ratio , 2015, ICIAR.

[70]  A. Vitale,et al.  Hypertension and the eye , 2008, Current opinion in ophthalmology.

[71]  A. Besga,et al.  Computer Aided Diagnosis system for Alzheimer Disease using brain Diffusion Tensor Imaging features selected by Pearson's correlation , 2011, Neuroscience Letters.

[72]  Ke Chen,et al.  Automated Vessel Segmentation Using Infinite Perimeter Active Contour Model with Hybrid Region Information with Application to Retinal Images , 2015, IEEE Transactions on Medical Imaging.

[73]  J. Bisognano,et al.  The relationship between retinal microvascular abnormalities and coronary heart disease: a review. , 2010, The American journal of medicine.

[74]  Manuel G. Penedo,et al.  Automatic Arteriovenous Ratio Computation: Emulating the Experts , 2012, DoCEIS.

[75]  Farshad Fotouhi,et al.  Bias and stability of single variable classifiers for feature ranking and selection , 2014, Expert Syst. Appl..

[76]  Manuel G. Penedo,et al.  Development of an automated system to classify retinal vessels into arteries and veins , 2012, Comput. Methods Programs Biomed..

[77]  Mong-Li Lee,et al.  Automatic grading of retinal vessel caliber , 2005, IEEE Transactions on Biomedical Engineering.

[78]  Chunlan Yang,et al.  Structural MRI-based detection of Alzheimer's disease using feature ranking and classification error , 2016, Comput. Methods Programs Biomed..