Combination of Enhanced Depth Imaging Optical Coherence Tomography and Fundus Images for Glaucoma Screening

Glaucoma is an eye disease that damages the optic nerve and can lead to irreversible loss of peripheral vision gradually and even blindness without treatment. Thus, diagnosing glaucoma in the early stage is essential for treatment. In this paper, an automatic method for early glaucoma screening is proposed. The proposed method combines structural parameters and textural features extracted from enhanced depth imaging optical coherence tomography (EDI-OCT) images and fundus images. The method first segments anterior the lamina cribrosa surface (ALCS) based on region-aware strategy and residual U-Net and then extracts structural features of the lamina cribrosa, such as lamina cribrosa depth and deformation of lamina cribrosa. In fundus images, scanning lines based on disc center and brightness reduction are used for optic disc segmentation and brightness compensation is utilized for segmenting the optic cup. Afterward, the cup-to-disc ratio (CDR) and textural features are extracted from fundus images. Hybrid features are used for training and classification to screen glaucoma by gcForest in the early stage. The proposed method has given exceptional results with 96.88% accuracy and 91.67% sensitivity.

[1]  M. Hangai,et al.  Lamina Cribrosa Depth Variation Measured by Spectral-Domain Optical Coherence Tomography Within and Between Four Glaucomatous Optic Disc Phenotypes. , 2015, Investigative ophthalmology & visual science.

[2]  Baihua Li,et al.  A Novel Adaptive Deformable Model for Automated Optic Disc and Cup Segmentation to Aid Glaucoma Diagnosis , 2017, Journal of Medical Systems.

[3]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[4]  Jayanthi Sivaswamy,et al.  Automatic glaucoma assessment from angio-OCT images , 2016, 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI).

[5]  ImageNet Classification with Deep Convolutional Neural , 2013 .

[6]  Ji Feng,et al.  Deep Forest: Towards An Alternative to Deep Neural Networks , 2017, IJCAI.

[7]  Jérôme Gilles,et al.  Empirical Wavelet Transform , 2013, IEEE Transactions on Signal Processing.

[8]  H. Quigley,et al.  The number of people with glaucoma worldwide in 2010 and 2020 , 2006, British Journal of Ophthalmology.

[9]  R. Ritch,et al.  In vivo evaluation of focal lamina cribrosa defects in glaucoma. , 2012, Archives of ophthalmology.

[10]  Thomas Brox,et al.  U-Net: Convolutional Networks for Biomedical Image Segmentation , 2015, MICCAI.

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

[12]  Ashish Issac,et al.  An automated and robust image processing algorithm for glaucoma diagnosis from fundus images using novel blood vessel tracking and bend point detection , 2018, Int. J. Medical Informatics.

[13]  Christopher Joseph Pal,et al.  The Importance of Skip Connections in Biomedical Image Segmentation , 2016, LABELS/DLMIA@MICCAI.

[14]  Rongchang Zhao,et al.  Retinal vessel optical coherence tomography images for anemia screening , 2018, Medical & Biological Engineering & Computing.

[15]  T. Wong,et al.  Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review and meta-analysis. , 2014, Ophthalmology.

[16]  Tahar Battikh,et al.  A new expert system based on fuzzy logic and image processing algorithms for early glaucoma diagnosis , 2018, Biomed. Signal Process. Control..

[17]  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.

[18]  Chan Kee Park,et al.  Diagnostic capability of lamina cribrosa thickness by enhanced depth imaging and factors affecting thickness in patients with glaucoma. , 2013, Ophthalmology.

[19]  M. Usman Akram,et al.  Improved automated detection of glaucoma from fundus image using hybrid structural and textural features , 2017, IET Image Process..

[20]  Gretchen A. Stevens,et al.  Causes of vision loss worldwide, 1990-2010: a systematic analysis. , 2013, The Lancet. Global health.

[21]  J. Mari,et al.  Lamina cribrosa visibility using optical coherence tomography: comparison of devices and effects of image enhancement techniques. , 2015, Investigative ophthalmology & visual science.

[22]  Weisi Lin,et al.  Automated anterior segment OCT image analysis for Angle Closure Glaucoma mechanisms classification , 2016, Comput. Methods Programs Biomed..

[23]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[24]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[25]  K. Park,et al.  Peripheral lamina cribrosa depth in primary open-angle glaucoma: a swept-source optical coherence tomography study of lamina cribrosa , 2015, Eye.

[26]  Alaa Eleyan,et al.  Co-occurrence based statistical approach for face recognition , 2009, 2009 24th International Symposium on Computer and Information Sciences.

[27]  Gretchen A. Stevens,et al.  Global prevalence of vision impairment and blindness: magnitude and temporal trends, 1990-2010. , 2013, Ophthalmology.

[28]  Xiaoyi Gao,et al.  Genetic Risk Score Is Associated with Vertical Cup-to-Disc Ratio and Improves Prediction of Primary Open-Angle Glaucoma in Latinos. , 2018, Ophthalmology.

[29]  R. Ritch,et al.  A Novel Method for Assessing Lamina Cribrosa Structure Ex Vivo Using Anterior Segment Enhanced Depth Imaging Optical Coherence Tomography , 2017, Journal of glaucoma.

[30]  J. Mari,et al.  Enhancement of lamina cribrosa visibility in optical coherence tomography images using adaptive compensation. , 2013, Investigative Ophthalmology and Visual Science.

[31]  Long Li,et al.  Study on the deformations of the lamina cribrosa during glaucoma. , 2017, Acta biomaterialia.

[32]  H. Quigley.  Number of people with glaucoma worldwide. , 1996, The British journal of ophthalmology.

[33]  Hao Wei,et al.  Automated retinal layer segmentation in OCT images of age-related macular degeneration , 2019, IET Image Process..

[34]  Peng Peng,et al.  Automatic Anterior Lamina Cribrosa Surface Depth Measurement Based on Active Contour and Energy Constraint , 2017, Journal of Computer Science and Technology.

[35]  Anjan Gudigar,et al.  Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images , 2018, Inf. Sci..

[36]  José Carlos Príncipe,et al.  Correntropy as a Novel Measure for Nonlinearity Tests , 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

[37]  C. Owsley,et al.  Comparison of Bruch's Membrane Opening Minimum Rim Width Among Those With Normal Ocular Health by Race. , 2017, American journal of ophthalmology.

[38]  Malay Kishore Dutta,et al.  An adaptive threshold based image processing technique for improved glaucoma detection and classification , 2015, Comput. Methods Programs Biomed..

[39]  Christopher Bowd,et al.  Automated segmentation of anterior lamina cribrosa surface: How the lamina cribrosa responds to intraocular pressure change in glaucoma eyes? , 2015, 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI).

[40]  Guang-ming Xian,et al.  An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM , 2010, Expert Syst. Appl..