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Classification of Glaucoma Stages Using Image Empirical Mode Decomposition from Fundus Images

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Abstract

One of the most prevalent causes of visual loss and blindness is glaucoma. Conventionally, instrument-based tools are employed for glaucoma screening. However, they are inefficient, time-consuming, and manual. Hence, computerized methodologies are needed for fast and accurate diagnosis of glaucoma. Therefore, we proposed a Computer-Aided Diagnosis (CAD) method for the classification of glaucoma stages using Image Empirical Mode decomposition (IEMD). In this study, IEMD is applied to decompose the preprocessed fundus photographs into different Intrinsic Mode Functions (IMFs) to capture the pixel variations. Then, the significant texture-based descriptors have been computed from the IMFs. A dimensionality reduction approach called Principal Component Analysis (PCA) has been employed to pick the robust descriptors from the retrieved feature set. We used the Analysis of Variance (ANOVA) test for feature ranking. Finally, the LS-SVM classifier has been employed to classify glaucoma stages. The proposed CAD system achieved a classification accuracy of 94.45% for the binary classification on the RIM-ONE r12 database. Our approach demonstrated better glaucoma classification performance than the existing automated systems.

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References

  1. Cheng J, Liu J, Xu Y et al., Super pixel classification based optic disc and optic cup segmentation for glaucoma screening, IEEE Transactions on Medical Imaging 32(6), 1019–1032, 2013.

    Article  PubMed  Google Scholar 

  2. Stella Mary MCV, Rajsingh EB, and Naik GR, Retinal fundus image analysis for diagnosis of glaucoma: A comprehensive survey, IEEE Access 4, 4327-4354, 2016.

    Article  Google Scholar 

  3. Narasimha-Iyer H et al., Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy, IEEE Transactions on Biomedical Engineering 53(6), 1084-1098, 2006.

    Article  PubMed  Google Scholar 

  4. Hagiwara Y et al., Computer-aided diagnosis of glaucoma using fundus images: A review, Computer Methods and Programs in Biomedicine, 165, 1-12, 2018.

    Article  PubMed  Google Scholar 

  5. Andres DP, Adrián C, Valery N, et al., Retinal image synthesis and semi-supervised learning for glaucoma assessment. IEEE Transactions on Medical Imaging 38(9), 2211-2218, 2019.

    Article  Google Scholar 

  6. Phan A, Truong P, Trumpp J, and Talke FE, Design of an Optical Pressure Measurement System for Intraocular Pressure Monitoring, IEEE Sensors Journal 18(1), 61-68, 2018.

    Article  CAS  Google Scholar 

  7. Aloudat M, Faezipour M, and El-Sayed A, Automated Vision-Based High Intraocular Pressure Detection Using Frontal Eye Images, IEEE Journal of Translational Engineering in Health and Medicine 7,1-13, 2019.

    Article  Google Scholar 

  8. Fu H, Cheng J, Xu Y el al., Disc-aware ensemble network for glaucoma screening from fundus image, IEEE Transactions on Medical Imaging 37 (11), 2493–2501, 2018.

  9. Lim TC, Chattopadhyay S, and Acharya UR, A survey and comparative study on the instruments for glaucoma detection, Med. Engg. Phys. 34, 129-139 2012.

    Article  Google Scholar 

  10. Parikh RS, Parikh SR, Kumar RS, Prabakaran S, Babu JG, and Thomas R, Diagnostic capability of scanning laser polarimetry with variable cornea compensator in Indian patients with early primary open-angle glaucoma, Ophthalmology 115(7) 1167-1172, 2008.

    Article  PubMed  Google Scholar 

  11. Abràmoff MD, Alward WL, Greenlee EC, et al., Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features, Invest Ophthalmol Vis Sci. 48 (4), 1665–1673, 2007.

    Article  PubMed  Google Scholar 

  12. Son J, Park SJ, and Jung K, Towards accurate segmentation of Retinal vessels and the optic disc in fundoscopic images with generative adversarial networks, J. Digit Imaging 32, 499–512, 2019.

    Article  PubMed  Google Scholar 

  13. Nyul LG, Retinal image analysis for automated glaucoma risk evaluation. Proc. SPIE. 7497, 1–9, 2009.

    Google Scholar 

  14. Nayak J, Acharya UR, Bhat PS, et al., Automated diagnosis of glaucoma using digital fundus images, J. Med. Syst. 33(5), 337–346, 2009.

    Article  PubMed  Google Scholar 

  15. Saha SK, Fernando B, Cuadros J et al., Automated quality assessment of colour fundus images for diabetic retinopathy screening in telemedicine. J Digit Imaging 31, 869–878, 2018.

    Article  PubMed  PubMed Central  Google Scholar 

  16. Dua S, Acharya UR, Chowriappa, P et al., Wavelet based energy features for glaucomatous image classification, IEEE Trans. Inf. Technol. Biomed. 6 (1), 80–87 , 2012.

    Article  Google Scholar 

  17. Kim PY, Iftekharuddin KM, Davey PG et al., Novel fractal feature based multiclass glaucoma detection and progression prediction, IEEE J. Biomed. Health Inform. 17 (2), 269–276, 2013.

    Article  PubMed  Google Scholar 

  18. Noronha KP, Acharya UR, Nayak K, et al., Automated classification of glaucoma stages using higher order cumulant features, Biomed. Signal Process. Control 10, 174–183, 2014.

    Article  Google Scholar 

  19. Acharya UR et al., Decision support system for the glaucoma using Gabor transformation, Biomed. Signal Process. Control 15, 18–26, 2015.

    Article  Google Scholar 

  20. Maheshwari S, Pachori RB, and Acharya UR, Automated diagnosis of glaucoma using empirical wavelet transform and correntropy features extracted from fundus images, IEEE J. Biomed. Health Inform. 21 (3), 803–813, 2017.

    Article  PubMed  Google Scholar 

  21. Maheshwari S, Kanhangad V, and Pachori RB, Iterative variational mode decomposition based automated detection of glaucoma using fundus images, Comput. Biol. Med. 88, 142-149, 2017.

    Article  PubMed  Google Scholar 

  22. Septiarini A, Harjoko A, Pulungan R et al., Optic disc and cup segmentation by automatic thresholding with morphological operation for glaucoma evaluation, Signal, Image and Video Processing 11, 945–952, 2017.

    Article  Google Scholar 

  23. Khowaja SA, Khowaja P, and Ismaili LA, A framework for retinal vessel segmentation from fundus images using hybrid feature set and hierarchical classification, Signal, Image and Video Processing 13 (2) 379-387, 2019.

    Article  Google Scholar 

  24. Agrawal DK, Kirar BS, and Pachori RB, Automated glaucoma detection using quasi-bivariate mode decomposition from fundus images, IET Image Process. 13 (13), 2401-2408, 2019.

    Article  Google Scholar 

  25. Li L, Xu M, Liu H et al., A Large-scale database and a CNN model for attention-based glaucoma detection, IEEE Trans. Med. Imag. 39 (2), 13-424, 2020.

    Article  Google Scholar 

  26. Parashar D and Agrawal DK, Automated classification of glaucoma stages using flexible analytic wavelet transform from retinal fundus images, IEEE Sensors Journal 20(21), 12885-12894, 2020.

    Article  Google Scholar 

  27. Huang NE, Shen Z, Long SR et al., The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. A: mathematical physical and engineering sciences, processing’s of the royal society. 454, 903–995, 1998.

  28. Linderhed A, Image empirical mode decomposition: a new tool for image processing, Advances in adaptive data analysis-world scientific 1 (2), 265-294, 2009.

    Article  Google Scholar 

  29. RIM-ONE Medical Image Analysis Group accessed: Dec. 12 2018. [Online]. available: http://medimrg.webs.ull.es/

  30. Chaudhary PK and Pachori RB, Automatic diagnosis of glaucoma using two-dimensional Fourier-Bessel series expansion based empirical wavelet transform, Biomed. Signal Process. Control 64, 102237, 2021.

    Article  Google Scholar 

  31. Ahn JM, Kim S, Ahn KS et. at., A deep learning model for the detection of both advanced and early glaucoma using fundus photography. PLoS ONE 13 (11), 1–8, 2018.

  32. Parashar D and Agrawal DK, 2-D compact variational mode decomposition-based automatic classification of glaucoma stages from fundus images, IEEE Transactions on Instrumentation and Measurement, 70, 1-10, 2021.

    Article  Google Scholar 

  33. Colominas MA, Schlotthauer G, and Torres ME, Improved complete ensemble EMD: A suitable tool for biomedical signal processing. Biomed. Sig. Process. Cont. 14, 19-29, 2014.

    Article  Google Scholar 

  34. Kirar BS, and Agrawal DK, Computer-aided diagnosis of glaucoma using discrete and empirical wavelet transform from fundus images. IET Image Process. 13 (1), 73-82, 2019.

    Article  Google Scholar 

  35. Hu Y, Liang Z, and Song B, Texture feature extraction and analysis for polyp differentiation via computed tomography colonography, IEEE Trans. Med. Imag. 35 (6), 1522-1531, 2016.

    Article  Google Scholar 

  36. Abdel-Hamid L, Glaucoma detection from retinal images using statistical and textural wavelet features, J. Digit Imaging 33, 151–158, 2020.

    Article  PubMed  Google Scholar 

  37. Yan S, Xu D, Zhang B et al., Graph embedding and extensions: a general framework for dimensionality reduction, IEEE Trans. Pattern Anal. Mach. Intell. 29 (1), 40–51, 2006.

    Article  Google Scholar 

  38. Suykens JAK and Vandewalle J, Least squares support vector machine classifiers, Neural Process. Lett. 9 (3), 293–300, 1999.

    Article  Google Scholar 

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Acknowledgements

The authors are grateful to Dr. J. Raykawar (Government Hospital, Ratlam) for providing a collection of fundus photos and clinical support for our study. This work is an outcome of the research and development project under the Technical Education Quality Improvement Program, A National Project Implementation Department of the Ministry of Education, the Government of India, to implement World Bank–supported projects in the technical field.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, IES College of Technology, Bhopal, 462044, MP, India

    Deepak Parashar

  2. Department of Electronics and Communication Engineering, Maulana Azad National Institute of Technology, Bhopal, 462003, MP, India

    Deepak Parashar & Dheraj Kumar Agrawal

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  1. Deepak Parashar
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  2. Dheraj Kumar Agrawal
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Correspondence to Deepak Parashar.

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Parashar, D., Agrawal, D.K. Classification of Glaucoma Stages Using Image Empirical Mode Decomposition from Fundus Images. J Digit Imaging 35, 1283–1292 (2022). https://doi.org/10.1007/s10278-022-00648-1

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  • DOI: https://doi.org/10.1007/s10278-022-00648-1

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