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Investigations of severity level measurements for diabetic macular oedema using machine learning algorithms

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Abstract

Background

The macula is an important part of the human visual system and is responsible for clear and colour vision. Macular oedema happens when fluid and protein deposit on or below the macula of the eye and cause the macula to thicken and swell. Normally, it occurs due to diabetes called diabetic macular oedema. Diabetic macular oedema (DME) is one of the main causes of visual impairment in patients.

Aim

The aims of the present study are to detect and localize abnormalities in blood vessels with respect to macula in order to prevent vision loss for the diabetic patients.

Methods

In this work, a novel fully computerized algorithm is used for the recognition of various diseases in macula using both fundus images and optical coherence tomography (OCT) images. Abnormal blood vessels are segmented using thresholding algorithm. The classification is performed by three different classifiers, namely, the support vector machine (SVM), cascade neural network (CNN) and partial least square (PLS) classifiers, which are employed to identify whether the image is normal or abnormal.

Conclusion

The results of all of the classifiers are compared based on their accuracy. The classifier accuracies of the SVM, cascade neural network and partial least square are 98.33, 97.16 and 94.34%, respectively. While analysing DME using both images, OCT produced efficient output than fundus images. Information about the severity of the disease and the localization of the pathologies is very useful to the ophthalmologist for diagnosing disease and choosing the proper treatment for a patient to prevent vision loss.

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References

  1. Acharya UR, Chua KC, Ng EYK (2008) Application of higher order spectra for the identification of diabetes retinopathy stages. J Med Syst 32:481–488

    Article  Google Scholar 

  2. Agurto C, Murray V, Barriga E (2010) Multiscale AM-FM methods for diabetic retinopathy lesion detection. IEEE Trans Med Imaging 29:502–512

    Article  PubMed  PubMed Central  Google Scholar 

  3. Spharak A, Uyyanonvara B, Barman S (2009) Automatic exudates detection from non-dilated diabetic retinopathy retinal images using fuzzy C-means clustering. Sensors 9:2148–2161

    Article  Google Scholar 

  4. Aliaa Abdel-Haleim Abdel-Razik Youssif, Atef Zaki Ghalwash, and Amr Ahmed Sabry Abdel-Rahman Ghoneim (2008). Optic disc detection from normalized digital fundus images by means of a vessels direction matched filter, IEEE TRANSACTIONS ON MEDICAL IMAGING, 27.

  5. Tariq A, Usman Akram M, Shaukat A, Shoab Khan A (2013) Automated detection and grading of diabetic maculopathy in digital retinal images. J Digit Imaging 4:803–812

    Article  Google Scholar 

  6. Antal B, Hajdu A (2012) An ensemble-based system for microaneurysm detection and diabetic retinopathy grading. IEEE Trans Biomed Eng 59:1720–1726

    Article  PubMed  Google Scholar 

  7. Brankin, E. Muldrew, A., Black, N. (2006) The optimization of thresholding technique for the identification of choroidal neovascular membranes in exudative age-related macular degeneration. IEEE Symp Computer Based Medical System, Salt Lake City, US, Lee DJ, Nutter B, Antani S (eds.) IEEE Computer Society Press. 430–435.

  8. Browning DJ, McOwen MD, Bowen RM Jr, O’Marah TL (2004) Comparison of the clinical diagnosis of diabetic macular edema with diagnosis by optical coherence tomography. Ophthalmology 111:712–715

    Article  PubMed  Google Scholar 

  9. Davis MD, Bressler SB, Aiello LP, Bressler NM, Browning DJ (2008) Comparison of time-domain OCT and fundus photographic assessments of retinal thickening in eyes with diabetic macular edema. Investig Ophthalmol 49:1745–1752

    Article  Google Scholar 

  10. Clara IS, Maria G, Mayo A, Maria IL, Hornero R (2009) Retinal image analysis based on mixture models to detect hard exudates. Med Image Anal 13(4):650–658

    Article  Google Scholar 

  11. Deepak K, Sivaswamy J (2012) Automatic assessment of macular oedema from colour retinal images. IEEE Trans Med Imag 31:766–776

    Article  Google Scholar 

  12. Ege BM, Hejlese OK, Larsen OV, Moller K, Jennings B, Kerr D, Cavan DA (2000) Screening for diabetic retinopathy using computer based image analysis and statistical classification. Comput Meth Prog Bio 62:165–175

    Article  CAS  Google Scholar 

  13. Foracchia, M., Grisan, E., and Ruggeri, A. (2004) Detection of optic disc in retinal images by means of a geometrical model of vessel structure, IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 23, NO. 10

  14. Gardner KG, Williamson D, Elliot AT (1996) Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Brit J Ophthalmol 11:940–944

    Article  Google Scholar 

  15. Giancardo L, Ruggeri A, Chaum E (2011) A textureless macula swelling detection with multiple retinal fundus images. IEEE Trans Biomed Eng 58:795–799

    Article  PubMed  Google Scholar 

  16. Grisan, E., Ruggeri A, (2007). Segmentation of candidate dark lesions in fundus images based on local thresholding and pixel density, 29th Annual Int Conf IEEE EMBS, Lyon, France, Rousseau J, Delhomme G, Akay M (eds.) IEEE Engineering in Medicine and Biological Society 6735–6738.

  17. Soares JVB, Leandro JJG, Cesar RM Jr, Jelinek HF, Michael Cree J (2006) Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE T Med Imaging 25:1214–1222

    Article  Google Scholar 

  18. Kande, G.B., Subbaiah, V., Savithri, T.S. (2008) Segmentation of exudates and optic disc in retinal images, IEEE Conf Computer Vision, Graphics and Image Processing, Kolkata, India, Sural S (ed.) IEEE Computer Society. 535–542.

  19. Ram K, Joshi GD, Sivaswamy J (2011) A successive clutter rejection based approach for early detection of diabetic retinopathy. IEEE Trans Biomed Eng 58:664–673

    Article  PubMed  Google Scholar 

  20. Kenneth, W., Tobin Edward Chaum V., Priya Govindasamy, Thomas Karnowski, P (2007) Detection of anatomic structures in human retinal imagery, IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 26, NO. 12.

  21. Krishnan, J.D.R., Kumar, A.S. (2008) Neural network based retinal image analysis. IEEE Conference on Image and Signal Processing, Sanya, China. 49–53.

  22. Noronha K, Navya KT, Nayak PK (2013) Hypertensive retinopathy. International Journal of Computer Applications 1:7–11

    Google Scholar 

  23. Mahendran G, Dhanasekaran R (2015) Investigation of abnormalities for diabetic retinopathy. Int J Appl Eng Res 10:963–967

    Google Scholar 

  24. Murugeswari S, Sukanesh R (2015) Investigations on diabetic macular edema using motion pattern estimation to prevent vision loss ARPN. Journal of Engineering and Applied Sciences 10(7):2940–2947

    Google Scholar 

  25. Osareh, A. Mirmehdi, M., Thomas, B. (2002). Comparative exudates classification using support vector machine and neural networks. 5th International Conference on Medical Image Computing-Assisted Intervention, Tokyo, Japan. 413–420.

  26. Meer P, Comaniciu D (2002) Mean shift robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619

    Article  Google Scholar 

  27. Reza AW, Hati CES (2008) Automatic tracing of optic disc and exudates from colour fundus images using fixed and variables thresholds. J Med Syst 33:73–80

    Article  Google Scholar 

  28. Shijian Lu (2011) Accurate and Efficient Optic Disc Detection and Segmentation by a Circular Transformation” IEEE Transactions on Medical Imaging 30(12):2126–2133

  29. Tang L, Niemeijer M, Reinhardt JM (2013) Splat feature classification with application to retinal haemorrhage detection in fundus images. IEEE Trans Med Imaging 32:364–375

    Article  PubMed  Google Scholar 

  30. Usher D, Dumsky M, Himaga M (2004) Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening. Diabet Med 21:84–90

    Article  CAS  PubMed  Google Scholar 

  31. Murugeswari, S., Sukanesh, R. (2016), Assessment of severity level for diabetic macular oedema using machine learning algorithms circuits and systems, 7, 1098–1105.

  32. Akram, MU, Khalid, S, Tariq, A, Khan, SA & Azam, F 2014, ‘Detection and Classification of Retinal Lesions for Grading of Diabetic Retinopathy’, Computers in Biology and Medicine 45(1):161–171

  33. Priya, R & Aruna, P (2012) SVM and neural network based diagnosis of diabetic retinopathy’, International Journal of Computer Applications 41(1):6–12

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Authors and Affiliations

  1. Syed Ammal Engineering College, Ramanathapuram, Tamil Nadu, India

    S. Murugeswari

  2. Thiagarajar College of Engineering, Madurai, India

    R. Sukanesh

Authors
  1. S. Murugeswari
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  2. R. Sukanesh
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Correspondence to S. Murugeswari.

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Murugeswari, S., Sukanesh, R. Investigations of severity level measurements for diabetic macular oedema using machine learning algorithms. Ir J Med Sci 186, 929–938 (2017). https://doi.org/10.1007/s11845-017-1598-8

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  • DOI: https://doi.org/10.1007/s11845-017-1598-8

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