Automated detection of retinal health using PHOG and SURF features extracted from fundus images

AbstractMany health-related problems arise with aging. One of the diseases that is prevalent among the elderly is the loss of sight. Various eye diseases, namely age-related macular degeneration (AMD), diabetic retinopathy (DR), and glaucoma are the prime causes of vision loss as we grow old. Nevertheless, early detection of such eye diseases can impede the progression of this problem. Therefore, the elderly are encouraged to attend regular eye checkups for early detection of eye diseases. However, it is time-consuming and laborious to conduct a mass eye screening session frequently. Hence, we proposed a novel approach to develop an automated retinal health screening system in this work. This paper discusses a retinal screening system to automatically differentiate normal image from abnormal (AMD, DR, and glaucoma) fundus images. The fundus images are subjected to the pyramid histogram of oriented gradients (PHOG) and speeded up robust features (SURF) techniques. Then, the extracted data are subjected to adaptive synthetic sampling to balance the number of data in the two classes (normal and abnormal). Subsequently, we employed the canonical correlation analysis approach to fuse the highly-correlated features extracted from the two (PHOG and SURF) descriptors. We have achieved 96.21% accuracy, 95.00% sensitivity, and 97.42% specificity with ten-fold cross-validation strategy using k-nearest neighbor (kNN) classifier. This novel algorithm has high potential in the diagnosis of normal eyes during the mass eye screening session or in polyclinics quickly and reliably. Hence, the patients having abnormal eyes can be sent to the main hospitals which will reduce the workload for the ophthalmologists. Graphical AbstractProposed system

[1]  Jasjit S. Suri,et al.  Image modeling of the human eye , 2008 .

[2]  U. Rajendra Acharya,et al.  Automated diabetic macular edema (DME) grading system using DWT, DCT Features and maculopathy index , 2017, Comput. Biol. Medicine.

[3]  Frans Coenen,et al.  Data Mining for AMD Screening: A Classification Based Approach , 2020 .

[4]  U. Rajendra Acharya,et al.  Automated screening system for retinal health using bi-dimensional empirical mode decomposition and integrated index , 2016, Comput. Biol. Medicine.

[5]  Frans Coenen,et al.  Data mining techniques for the screening of age-related macular degeneration , 2012, Knowl. Based Syst..

[6]  U. Rajendra Acharya,et al.  Iterative variational mode decomposition based automated detection of glaucoma using fundus images , 2017, Comput. Biol. Medicine.

[7]  U. Rajendra Acharya,et al.  Computer-aided diabetic retinopathy detection using trace transforms on digital fundus images , 2014, Medical & Biological Engineering & Computing.

[8]  U. Rajendra Acharya,et al.  Automated screening tool for dry and wet age-related macular degeneration (ARMD) using pyramid of histogram of oriented gradients (PHOG) and nonlinear features , 2017, J. Comput. Sci..

[9]  Kevin Noronha,et al.  Local configuration pattern features for age-related macular degeneration characterization and classification , 2015, Comput. Biol. Medicine.

[10]  Ali M. Reza,et al.  Realization of the Contrast Limited Adaptive Histogram Equalization (CLAHE) for Real-Time Image Enhancement , 2004, J. VLSI Signal Process..

[11]  James Kennedy,et al.  Particle swarm optimization , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[12]  David G. Stork,et al.  Pattern Classification , 1973 .

[13]  Kevin Noronha,et al.  Decision support system for the glaucoma using Gabor transformation , 2015, Biomed. Signal Process. Control..

[14]  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..

[15]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[16]  Yan Liu,et al.  A new method of feature fusion and its application in image recognition , 2005, Pattern Recognit..

[17]  U. Rajendra Acharya,et al.  A novel algorithm to detect glaucoma risk using texton and local configuration pattern features extracted from fundus images , 2017, Comput. Biol. Medicine.

[18]  Kevin Noronha,et al.  Automated detection of age-related macular degeneration using empirical mode decomposition , 2015, Knowl. Based Syst..

[19]  David Keating,et al.  Automatic detection ofdiabetic retinopathy using anartificial neural network: ascreening tool , 1996 .

[20]  Selwyn Piramuthu,et al.  Artificial Intelligence and Information Technology Evaluating feature selection methods for learning in data mining applications , 2004 .

[21]  Haibo He,et al.  ADASYN: Adaptive synthetic sampling approach for imbalanced learning , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[22]  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.

[23]  M. Abràmoff,et al.  Web-based screening for diabetic retinopathy in a primary care population: the EyeCheck project. , 2005, Telemedicine journal and e-health : the official journal of the American Telemedicine Association.

[24]  P. Kolar.  Classification and Clinical Features of AMD , 2013 .

[25]  U. Rajendra Acharya,et al.  An Integrated Index for the Identification of Diabetic Retinopathy Stages Using Texture Parameters , 2012, Journal of Medical Systems.

[26]  B. Holden,et al.  The Right to Sight , 2000, Clinical & experimental optometry.

[27]  Grading diabetic retinopathy from stereoscopic color fundus photographs--an extension of the modified Airlie House classification. ETDRS report number 10. Early Treatment Diabetic Retinopathy Study Research Group. , 1991, Ophthalmology.

[28]  U. Rajendra Acharya,et al.  Application of Higher Order Spectra for the Identification of Diabetes Retinopathy Stages , 2008, Journal of Medical Systems.

[29]  U. Rajendra Acharya,et al.  Wavelet-Based Energy Features for Glaucomatous Image Classification , 2012, IEEE Transactions on Information Technology in Biomedicine.

[30]  U. Rajendra Acharya,et al.  Automated Diagnosis of Glaucoma Using Digital Fundus Images , 2009, Journal of Medical Systems.

[31]  U R Acharya,et al.  Decision support system for diabetic retinopathy using discrete wavelet transform. , 2013, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[32]  José Salvador Sánchez,et al.  On the effectiveness of preprocessing methods when dealing with different levels of class imbalance , 2012, Knowl. Based Syst..

[33]  Tien Yin Wong,et al.  A survey on computer aided diagnosis for ocular diseases , 2014, BMC Medical Informatics and Decision Making.

[34]  Cemal Köse,et al.  A Statistical Segmentation Method for Measuring Age-Related Macular Degeneration in Retinal Fundus Images , 2010, Journal of Medical Systems.

[35]  U. Rajendra Acharya,et al.  Automated Identification of Diabetic Retinopathy Stages Using Digital Fundus Images , 2008, Journal of Medical Systems.

[36]  Andrew Zisserman,et al.  Representing shape with a spatial pyramid kernel , 2007, CIVR '07.

[37]  U. Rajendra Acharya,et al.  Algorithms for the Automated Detection of Diabetic Retinopathy Using Digital Fundus Images: A Review , 2012, Journal of Medical Systems.

[38]  U. Rajendra Acharya,et al.  Diagnosis of retinal health in digital fundus images using continuous wavelet transform (CWT) and entropies , 2017, Comput. Biol. Medicine.

[39]  Qin Li,et al.  Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs , 2010, IEEE Transactions on Medical Imaging.

[40]  R. Kolá.  Detection of Glaucomatous Eye via Color Fundus Images Using Fractal Dimensions , 2008 .

[41]  Augustinus Laude,et al.  Age-related Macular Degeneration - An Asian Perspective , 2007 .

[42]  Frans Coenen,et al.  Automated "disease/no disease" grading of age-related macular degeneration by an image mining approach. , 2012, Investigative ophthalmology & visual science.

[43]  László G. Nyúl,et al.  Glaucoma risk index:  Automated glaucoma detection from color fundus images , 2010, Medical Image Anal..

[44]  Kevin Noronha,et al.  Biomedical Signal Processing and Control Automated Classification of Glaucoma Stages Using Higher Order Cumulant Features , 2022 .

[45]  Victor Murray,et al.  Automatic detection of diabetic retinopathy and age-related macular degeneration in digital fundus images. , 2011, Investigative ophthalmology & visual science.

[46]  U. Rajendra Acharya,et al.  Automated Diagnosis of Glaucoma Using Texture and Higher Order Spectra Features , 2011, IEEE Transactions on Information Technology in Biomedicine.

[47]  Xin Wang,et al.  Image enhancement for radiography inspection , 2005, International Conference on Experimental Mechanics.

[48]  John D. Austin,et al.  Adaptive histogram equalization and its variations , 1987 .

[49]  U. Rajendra Acharya,et al.  Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features , 2012, Knowl. Based Syst..

[50]  Gwénolé Quellec,et al.  Automated early detection of diabetic retinopathy. , 2010, Ophthalmology.

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

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

[53]  Uğur Şevik,et al.  Automatic segmentation of age-related macular degeneration in retinal fundus images , 2008, Comput. Biol. Medicine.

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

[55]  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.

[56]  Kevin Noronha,et al.  Automated diagnosis of Age-related Macular Degeneration using greyscale features from digital fundus images , 2014, Comput. Biol. Medicine.

[57]  Kevin Noronha,et al.  Novel risk index for the identification of age-related macular degeneration using radon transform and DWT features , 2016, Comput. Biol. Medicine.

[58]  Kevin Noronha,et al.  Decision support system for age-related macular degeneration using discrete wavelet transform , 2014, Medical & Biological Engineering & Computing.

[59]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[60]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[61]  U. Rajendra Acharya,et al.  Segmentation of optic disc, fovea and retinal vasculature using a single convolutional neural network , 2017, J. Comput. Sci..

[62]  U. Rajendra Acharya,et al.  Automated Diagnosis of Glaucoma Using Empirical Wavelet Transform and Correntropy Features Extracted From Fundus Images , 2017, IEEE Journal of Biomedical and Health Informatics.

[63]  U. Rajendra Acharya,et al.  Identification of different stages of diabetic retinopathy using retinal optical images , 2008, Inf. Sci..