Hierarchical method for cataract grading based on retinal images using improved Haar wavelet

Abstract Cataracts, which are lenticular opacities that may occur at different lens locations, are the leading cause of visual impairment worldwide. Accurate and timely diagnosis can improve the quality of life of cataract patients. In this paper, a feature extraction-based method for grading cataract severity using retinal images is proposed. To obtain more appropriate features for the automatic grading, the Haar wavelet is improved according to the characteristics of retinal images. Retinal images of non-cataract, as well as mild, moderate, and severe cataracts, are automatically recognized using the improved Haar wavelet. A hierarchical strategy is used to transform the four-class classification problem into three adjacent two-class classification problems. Three sets of two-class classifiers based on a neural network are trained individually and integrated together to establish a complete classification system. The accuracies of the two-class classification (cataract and non-cataract) and four-class classification are 94.83% and 85.98%, respectively. The performance analysis demonstrates that the improved Haar wavelet feature achieves higher accuracy than the original Haar wavelet feature, and the fusion of three sets of two-class classifiers is superior to a simple four-class classifier. The discussion indicates that the retinal image-based method offers significant potential for cataract detection.

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

[2]  Natalie A. Afshari,et al.  Cataract grading systems: a review of past and present , 2019, Current opinion in ophthalmology.

[3]  Jianqiang Li,et al.  A computer-aided healthcare system for cataract classification and grading based on fundus image analysis , 2015, Comput. Ind..

[4]  Mark S. Nixon,et al.  Water flow based vessel detection in retinal images , 2006 .

[5]  Deguang Han,et al.  On the Orthogonality of Frames and the Density and Connectivity of Wavelet Frames , 2009 .

[6]  Youngsub Eom,et al.  Effect of Cataract Grade according to Wide-Field Fundus Images on Measurement of Macular Thickness in Cataract Patients , 2018, Korean journal of ophthalmology : KJO.

[7]  Jianqiang Li,et al.  Automatic cataract detection and grading using Deep Convolutional Neural Network , 2017, 2017 IEEE 14th International Conference on Networking, Sensing and Control (ICNSC).

[8]  Tomi Kauppi,et al.  Eye Fundus Image Analysis for Automatic Detection of Diabetic Retinopathy , 2010 .

[9]  Ulrich Meyer,et al.  On the Three-Dimensional Physiological Position of the Temporomandibular Joint , 2004, Journal of Orofacial Orthopedics / Fortschritte der Kieferorthopädie.

[10]  Thomas F Hany,et al.  Non-Hodgkin lymphoma and Hodgkin disease: coregistered FDG PET and CT at staging and restaging--do we need contrast-enhanced CT? , 2004, Radiology.

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

[12]  V. Dragomirescu,et al.  Measurements of lens transparency or its disturbances by densitometric image analysis of scheimpflug photographs , 2004, Graefe's Archive for Clinical and Experimental Ophthalmology.

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

[14]  Jun Cheng,et al.  Sparse Range-Constrained Learning and Its Application for Medical Image Grading , 2018, IEEE Transactions on Medical Imaging.

[15]  A. R. Hill,et al.  The Oxford Clinical Cataract Classification and Grading System , 1986, International Ophthalmology.

[16]  Kenneth W. Tobin,et al.  Exudate-based diabetic macular edema detection in fundus images using publicly available datasets , 2012, Medical Image Anal..

[17]  D. Pascolini,et al.  Global estimates of visual impairment: 2010 , 2011, British Journal of Ophthalmology.

[18]  Miguel Caixinha,et al.  New approach for objective cataract classification based on ultrasound techniques using multiclass SVM classifiers , 2014, 2014 IEEE International Ultrasonics Symposium.

[19]  S. West,et al.  Use of photographic techniques to grade nuclear cataracts. , 1988, Investigative ophthalmology & visual science.

[20]  Tzyy-Ping Jung,et al.  Glaucoma Progression Detection Using Structural Retinal Nerve Fiber Layer Measurements and Functional Visual Field Points , 2014, IEEE Transactions on Biomedical Engineering.

[21]  AcharyaU. Rajendra,et al.  Computer-aided diagnosis of diabetic retinopathy , 2013 .

[22]  L. Chylack,et al.  Classification of human senile cataractous changes by the American Cooperative Cataract Research Group (CCRG) method. I. Instrumentation and technique. , 1983, Investigative ophthalmology & visual science.

[23]  A. Molteno,et al.  Fourier analysis of digital retinal images in estimation of cataract severity , 2008, Clinical & experimental ophthalmology.

[24]  Joewono Widjaja,et al.  Experimental demonstrations of noise-robustness of compression-based joint wavelet transform correlator in retinal recognition , 2017 .

[25]  Jialiang Li,et al.  Cataract Prevalence Varies Substantially with Assessment Systems: Comparison of Clinical and Photographic Grading in a Population-Based Study , 2011, Ophthalmic epidemiology.

[26]  A. Foster.  Vision 2020: the cataract challenge. , 2000, Community eye health.

[27]  U. Rajendra Acharya,et al.  Computer-aided diagnosis of diabetic retinopathy: A review , 2013, Comput. Biol. Medicine.

[28]  S. N. Deepa,et al.  Comparative analysis on hidden neurons estimation in multi layer perceptron neural networks for wind speed forecasting , 2017, Artificial Intelligence Review.

[29]  Miguel Caixinha,et al.  Using Ultrasound Backscattering Signals and Nakagami Statistical Distribution to Assess Regional Cataract Hardness , 2014, IEEE Transactions on Biomedical Engineering.

[30]  Anam Tariq,et al.  Survey Analysis of Automatic Detection and Grading of Cataract Using Different Imaging Modalities , 2018, Applications of Intelligent Technologies in Healthcare.

[31]  Filip Sroubek,et al.  Retinal image restoration by means of blind deconvolution. , 2011, Journal of biomedical optics.

[32]  Tien Yin Wong,et al.  Lens image registration for cataract detection , 2011, 2011 6th IEEE Conference on Industrial Electronics and Applications.

[33]  Elena Velte,et al.  Automatic Cataract Classification based on Ultrasound Technique Using Machine Learning: A comparative Study , 2015 .

[34]  J. R. Landis,et al.  The measurement of observer agreement for categorical data. , 1977, Biometrics.

[35]  Brigitte Röder,et al.  Increased visual cortical thickness in sight‐recovery individuals , 2015, Human brain mapping.

[36]  Huiqi Li,et al.  Automated segmentation of overlapped nuclei using concave point detection and segment grouping , 2017, Pattern Recognit..

[37]  Song Xu-don,et al.  The study of predicting the visual acuity after phacoemulsification according to the blur level of fundus photography , 2010 .

[38]  M. C. Leske,et al.  The Lens Opacities Classification System III. The Longitudinal Study of Cataract Study Group. , 1993, Archives of ophthalmology.

[39]  Samir I Sayegh,et al.  Cataract screening by minimally trained remote observer with non-mydriatic digital fundus camera , 2013, Photonics West - Biomedical Optics.

[40]  Vishal Arora,et al.  Microscope Integrated Intraoperative Spectral Domain Optical Coherence Tomography for Cataract Surgery: Uses and Applications , 2015, Current eye research.

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

[42]  Zhiming Wang,et al.  No-reference image blur assessment based on local total variation , 2016, 2016 IEEE International Conference on Signal and Image Processing (ICSIP).

[43]  Jianqiang Li,et al.  Classification of retinal image for automatic cataract detection , 2013, 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013).

[44]  Wang Ya.  Cataract screening as a key step to vision restoring project in country , 2010 .

[45]  M. Sonka,et al.  Retinal Imaging and Image Analysis , 2010, IEEE Reviews in Biomedical Engineering.

[46]  Luiz Carlos Rodrigues,et al.  Segmentation of optic disc and blood vessels in retinal images using wavelets, mathematical morphology and Hessian-based multi-scale filtering , 2017, Biomed. Signal Process. Control..

[47]  Biju Raju,et al.  Do it yourself smartphone fundus camera – DIYretCAM , 2016, Indian journal of ophthalmology.

[48]  Song Xu-don,et al.  The evaluation of screening for cataract needed surgery with digital nonmydriatic fundus camera , 2010 .

[49]  Gwénolé Quellec,et al.  Optimal Wavelet Transform for the Detection of Microaneurysms in Retina Photographs , 2008, IEEE Transactions on Medical Imaging.

[50]  Jacob Cohen.  A Coefficient of Agreement for Nominal Scales , 1960 .

[51]  Parismita Bharali,et al.  Automated Detection of Diabetic Retinopathy , 2019, 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP).

[52]  Guo Qian,et al.  Forecasting the Rural Per Capita Living Consumption Based on Matlab BP Neural Network , 2013 .

[53]  Miguel Caixinha,et al.  In-Vivo Automatic Nuclear Cataract Detection and Classification in an Animal Model by Ultrasounds , 2016, IEEE Transactions on Biomedical Engineering.

[54]  Li Xiong,et al.  An Approach to Evaluate Blurriness in Retinal Images with Vitreous Opacity for Cataract Diagnosis , 2017, Journal of healthcare engineering.

[55]  Jianqiang Li,et al.  Principal component analysis based cataract grading and classification , 2015, 2015 17th International Conference on E-health Networking, Application & Services (HealthCom).

[56]  Miguel Caixinha,et al.  Automatic Cataract Hardness Classification Ex Vivo by Ultrasound Techniques. , 2016, Ultrasound in medicine & biology.

[57]  M. C. Leske,et al.  The Lens Opacities Classification System III , 1993 .

[58]  Allen Foster,et al.  Results of cataract surgery in young children in east Africa , 2001, The British journal of ophthalmology.

[59]  Xu Lian.  A new method of cataract imaging evaluation , 2010 .

[60]  P. Burlina,et al.  Automated classification of severity of age-related macular degeneration from fundus photographs. , 2013, Investigative ophthalmology & visual science.

[61]  Jianqiang Li,et al.  Exploiting ensemble learning for automatic cataract detection and grading , 2016, Comput. Methods Programs Biomed..

[62]  L T Chylack,et al.  Evaluation of lens opacities classification system III applied at the slitlamp. , 1993, Optometry and vision science : official publication of the American Academy of Optometry.

[63]  H. Kundel,et al.  Measurement of observer agreement. , 2003, Radiology.

[64]  Danail Stoyanov,et al.  DeepPhase: Surgical Phase Recognition in CATARACTS Videos , 2018, MICCAI.

[65]  Huiqi Li,et al.  Lens opacity detection for serious posterior subcapsular cataract , 2017, Medical & Biological Engineering & Computing.

[66]  H. Taylor,et al.  Number of People Blind or Visually Impaired by Cataract Worldwide and in World Regions, 1990 to 2010. , 2015, Investigative ophthalmology & visual science.

[67]  R. Gunasundari,et al.  Computer-Aided Diagnosis of Anterior Segment Eye Abnormalities using Visible Wavelength Image Analysis Based Machine Learning , 2018, Journal of Medical Systems.

[68]  Mark E Pennesi,et al.  Management of plateau iris syndrome with cataract extraction and endoscopic cyclophotocoagulation , 2017, Experimental eye research.