Diagnosis of diabetic retinopathy based on holistic texture and local retinal features

Abstract In this paper, eye fundus images are analyzed for the automatic detection of diabetic retinopathy. One thousand two hundred eye fundus images of the Messidor database were used to test the system using the cross validation in various settings. Two types of features were extracted including the holistic texture features and the local retinal features. Four classifiers were implemented including the k-nearest neighbors, neural networks, support vector machines, and random decision forests. The best results from the analysis of holistic texture features were obtained for the Independent Component Analysis method, which had never been tested before in this type of image. Furthermore, the performance of our system improved greatly when two local retinal features — micro-aneurysms and exudates — were incorporated into the analysis, a methodology inspired by a modular approach originally developed for face-recognition tasks. The diagnostic performance of our algorithm is very promising and similar to previous automatic systems and human expert analysis on the same dataset. This framework has the potential to be used as an aiding tool for the diagnosis of diabetic retinopathy.

[1]  Bram van Ginneken,et al.  Automatic detection of red lesions in digital color fundus photographs , 2005, IEEE Transactions on Medical Imaging.

[2]  Bruce A. Draper,et al.  Recognizing faces with PCA and ICA , 2003, Comput. Vis. Image Underst..

[3]  Bunyarit Uyyanonvara,et al.  Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods , 2008, Comput. Medical Imaging Graph..

[4]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[5]  Alex Pentland,et al.  Face recognition using view-based and modular eigenspaces , 1994, Optics & Photonics.

[6]  Gernot A. Fink,et al.  Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images , 2017, 2017 IEEE International Conference on Image Processing (ICIP).

[7]  B. van Ginneken,et al.  Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. , 2007, Investigative ophthalmology & visual science.

[8]  Luming Zhang,et al.  Action2Activity: Recognizing Complex Activities from Sensor Data , 2015, IJCAI.

[9]  Enrico Grisan,et al.  Luminosity and contrast normalization in retinal images , 2005, Medical Image Anal..

[10]  Keerthi Ram,et al.  Local characterization of neovascularization and identification of proliferative diabetic retinopathy in retinal fundus images , 2017, Comput. Medical Imaging Graph..

[11]  Aapo Hyvärinen,et al.  Fast and robust fixed-point algorithms for independent component analysis , 1999, IEEE Trans. Neural Networks.

[12]  J. Boyce,et al.  Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening , 2004, Diabetic medicine : a journal of the British Diabetic Association.

[13]  Prateek Prasanna,et al.  Decision support system for detection of diabetic retinopathy using smartphones , 2013, 2013 7th International Conference on Pervasive Computing Technologies for Healthcare and Workshops.

[14]  C. Sinthanayothin,et al.  Automated detection of diabetic retinopathy on digital fundus images , 2002, Diabetic medicine : a journal of the British Diabetic Association.

[15]  Mislav Grgic,et al.  Independent comparative study of PCA, ICA, and LDA on the FERET data set , 2005, Int. J. Imaging Syst. Technol..

[16]  Marios S. Pattichis,et al.  Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection , 2010, IEEE Transactions on Medical Imaging.

[17]  Erkki Oja,et al.  The FastICA Algorithm Revisited: Convergence Analysis , 2006, IEEE Transactions on Neural Networks.

[18]  Bálint Antal,et al.  An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading , 2012, IEEE Transactions on Biomedical Engineering.

[19]  C. M. Lim,et al.  Computer-based detection of diabetes retinopathy stages using digital fundus images , 2009, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[20]  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.

[21]  Noemi Lois,et al.  The progress in understanding and treatment of diabetic retinopathy , 2016, Progress in Retinal and Eye Research.

[22]  Estefanía Cortés-Ancos,et al.  Microaneurysm Candidate Extraction Methodology in Retinal Images for the Integration into Classification-Based Detection Systems , 2017, IWBBIO.

[23]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[24]  R. Vanithamani,et al.  Exudates in Detection and Classification of Diabetic Retinopathy , 2016, SoCPaR.

[25]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[26]  Meindert Niemeijer,et al.  Evaluation of a computer-aided diagnosis system for diabetic retinopathy screening on public data. , 2011, Investigative ophthalmology & visual science.

[27]  Pascale Massin,et al.  Automatic detection of microaneurysms in color fundus images , 2007, Medical Image Anal..

[28]  Roberto Hornero,et al.  Automated detection of diabetic retinopathy in retinal images , 2016, Indian journal of ophthalmology.

[29]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[30]  Roberto Hornero,et al.  A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis. , 2008, Medical engineering & physics.

[31]  Keshab K. Parhi,et al.  DREAM: Diabetic Retinopathy Analysis Using Machine Learning , 2014, IEEE Journal of Biomedical and Health Informatics.

[32]  Marian Stewart Bartlett,et al.  Face recognition by independent component analysis , 2002, IEEE Trans. Neural Networks.

[33]  Luming Zhang,et al.  Fortune Teller: Predicting Your Career Path , 2016, AAAI.

[34]  R Aishwarya,et al.  A Hybrid Classifier for the Detection of Microaneurysms in Diabetic Retinal Images , 2017 .

[35]  Yu Zheng,et al.  Urban Water Quality Prediction Based on Multi-Task Multi-View Learning , 2016, IJCAI.

[36]  M. Turk,et al.  Eigenfaces for Recognition , 1991, Journal of Cognitive Neuroscience.

[37]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.