Glaucoma diagnosis using discrete wavelet transform and histogram features from fundus images

 
 
 
  • Abstract
  • Keywords
  • References
  • PDF
  • Abstract


    Glaucoma is one of the main eye diseases; it cause progressive deterioration of optic nerve fibers due to increased fluid pressure. The existing methods of glaucoma diagnosis are time consuming, expensive and require practiced clinicians to understand the eye problems. Hence fast, cheap and more accurate glaucoma diagnosis methods are needed. This paper presents an innovative idea for diagnosis of glaucoma using third level two dimensional discrete wavelet transform (2D DWT) and histogram features from fundus images. The 2D DWT is used to decompose the glaucoma and healthy images and histogram features are extracted from 2D DWT decomposed sub band images. The least square support vector machine (LS-SVM) is used as a classifier which classifies the glaucoma and healthy images using the extracted features. The proposed method yielded classification accuracy of 88.33%, 87.50%, and 86.67% for ten, eight and fivefold cross validation respectively. The obtained classification accuracy, sensitivity and specificity are 88.33%, 90.00%, and 85.00% for tenfold cross validation respectively. Obtained results prove that the performance of the proposed method is better compared to the existing methods. It may considerably increases the diagnosis speed of ophthalmologists.


  • Keywords


    Discrete Wavelet Transform; Feature Extraction; Glaucoma; Support Vector Machine; Pre-Processing.

  • References


      [1] Bock R, Meier J, Ny´ul LG, Hornegger J, & Michelson G, “Glaucoma risk index: Atomated glaucoma detection from color fundus images”, Medical Image Analysis, Vol. 14, No. 3, (2010), pp.471-481.https://doi.org/10.1016/j.media.2009.12.006.

      [2] Tham YC, Li X, Wong TY, Quigley HA, Aung T & Cheng CY, “Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis”, Ophthalmology, Vol. 121, No.11, (2014), pp. 2081–2090.https://doi.org/10.1016/j.ophtha.2014.05.013.

      [3] Shen SY, Wong TY, Foster PJ, Loo JL, Rosman M, Loon SC, Wong WL, Saw SM & Aung T, “The prevalence and types of glaucoma in Malay people: The Singapore Malay eye study”, Investigative Ophthalmology and Visual Science, Vol. 49, No. 9, (2008), pp. 3846–3851.https://doi.org/10.1167/iovs.08-1759.

      [4] Resnikoff S, Pascolini D, Etya'ale D, Kocur I, Pararajasegaram R , Pokharel GP &Mariotti SP, “Global data on visual impairment in the year 2002”, Bulletin of the World Health Organization, Vol. 82, No.11, ( 2004).

      [5] Nayak J, Acharya UR, Bhat PS, Shetty A & Lim TC, “Automated diagnosis of glaucoma using digital fundus images”, J. Med. Syst., Vol. 33, No. 5, (2009), pp. 337–346.https://doi.org/10.1007/s10916-008-9195-z.

      [6] Types of glaucoma, available online: https://www.glaucoma.org/glaucoma/types-of-glaucoma.php, last visit: 08.01.2017.

      [7] Kavitha S, Zebardast N, Palaniswamy K, Wojciechowski R, Chan ES, Friedman DS, Venkatesh R &Ramulu PY, “Family history is a strong risk factor for prevalent angle closure in a south Indian population”, Ophthalmology, Vol. 121, No. 11, (2014), pp. 2091–2097.https://doi.org/10.1016/j.ophtha.2014.05.001.

      [8] Greaney MJ, Hoffman DC, Garway-Heath DF, Nakla M, Coleman AL, &Caprioli J , “Comparison of optic nerve imaging methods to distinguish normal eyes from those with glaucoma”, Investigative Ophthalmology and Visual Science, Vol. 43, No. 1, (2002), pp. 140–145.

      [9] Detecting glaucoma, available online: http://www.visionaware.org/info/your-eye-condition/glaucoma/detecting-glaucoma/125, last visit: 08.01.2017.

      [10] Kersey Thomas, Clement Colin I, Bloom Phillip &Cordeiro M. Francesca, “New trends in glaucoma risk, diagnosis & management”, Indian J Med Res., Vol. 137, (2013), pp 659-668.

      [11] Song X, Song K & Chen Y, “A computer-based diagnosis system for early glaucoma screening”, Proceedings of the 27th Annual IEEE Engineering in Medicine and Biology, (2005), pp. 6608– 6611.

      [12] Dua S, Acharya UR, Chowriappa P &Sree VS, “Wavelet based energy features for glaucomatous image classification”, IEEE Transactions on Information Technology in Biomedicine, Vol. 16, No. 1, (2012), pp. 80–87.https://doi.org/10.1109/TITB.2011.2176540.

      [13] Lim TC, Chattopadhyay S & Acharya UR, “A survey and comparative study on the instruments for glaucoma detection”, Medical Engineering & Physics, Vol. 34, No. 2, (2012), pp.129-139.https://doi.org/10.1016/j.medengphy.2011.07.030.

      [14] Hermann MM, David F, Garway-Heath DF, Jonescu- Cuypers CP, Reinhard OW, Burk ROW, Jost B, Jonas JB, Mardin CY, Funk J &Diestelhorst M, “Interobserver variability in confocal optic nerve analysis (HRT),” Int. Ophthalmol., Vol. 26, No. 4, (2005), pp.5143–149.

      [15] Yadav D, Sarathi MP & Dutta MK, “Classification of glaucoma based on texture features using neural networks”, in IEEE 7th International Conference on Contemporary Computing, 2014.

      [16] Kolar R & Jan J, “Detection of glaucomatous eye via color fundus images using fractal dimensions”, Radio Engineering, Vol. 17, No. 3, (2008), pp. 109–114.

      [17] Raja C &Gangatharan N, “Glaucoma detection in fundal retinal images using trispectrum and complex wavelet based features”, European Journal of Scientific Research, Vol. 97 No. 1, (2013), pp. 159–171.

      [18] Raja C &Gangatharan N, “Appropriate sub-band selection in wavelet packet decomposition for automated glaucoma diagnoses”, International Journal of Automation and Computing, Vol. 12, No. 4, (2015), pp. 393-401.https://doi.org/10.1007/s11633-014-0858-6.

      [19] Townsend KA, Wollstein G, Danks D, Sung KR, Ishikawa H, Kagemann L, Gabriele ML &SchumanJS, “Heidelberg retina tomograph 3 machine learning classifiers for glaucoma detection”, Br. J. Ophthalmol., Vol. 92, No. 6, (2008), pp. 814–818. https://doi.org/10.1136/bjo.2007.133074.

      [20] Kim PY, Khan M. Iftekharuddin, Davey PG, M. T., Garas Anita, Hollo G, &Essock EA, “Novel fractal feature-based multiclass glaucoma detection and progression prediction”, IEEE Journal Of Biomedical And Health Informatics, Vol. 17, No.2, (2013), pp.269-276.

      [21] RIM1 Image dataset of Medical Image Analysis Group, available onlinehttp://medimrg.webs.ull.es, last visit: 28.07.2017.https://doi.org/10.1109/TITB.2012.2218661.

      [22] Hassan Rohayanti, KasimShahreen, Zubaidah WA, Wan ChekJafery, Shah ZA, “Image enhancement technique at different distance for iris recognition”, International Journal on Advanced Science, Engineering and Information Technology, Vol.7, (2017), pp. 4-2.

      [23] AlickovicEmina, Kevric Jasmin &SubasiAbdulhamit, “Performance evaluation of empirical mode decomposition, discrete wavelet transform, and wavelet packed decomposition for automated epileptic seizure detection and prediction”, Biomedical Signal Processing and Control, Vol. 39, (2018), pp. 94-102.https://doi.org/10.1016/j.bspc.2017.07.022.

      [24] Lahmiri Salim &BoukadoumMounir “Biomedical image denoising using variational mode decomposition”, Institute of Electrical and Electronics Engineers, Oct. 2014.

      [25] Chaplot S, Patnaik LM, Jagannathan NR, “Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network”, Biomed Signal Process Control, Vol. 1, No,1, (2006), pp. 86-92.https://doi.org/10.1016/j.bspc.2006.05.002.

      [26] Gonzalez RC & Woods RE, Digital image processing, Pearson, 2014.

      [27] Gonzalez RC, Woods RE &Eddins SL, Digital image processing Using Matlab, TMH Pvt. Ltd., 2010.

      [28] Kirar BS and Agrawal DK, “Empirical wavelet transform based preprocessing and entropy feature extraction from glaucomatous digital fundus images”, International Conference on Recent Innovations in Signal processing and Embedded Systems (RISE), Bhopal, 2017, pp. 315-319. https://doi.org/10.1109/RISE.2017.8378173.

      [29] Suykens JAK and Vandewalle J, “Least squares support vector machine classifiers”, Neural Processing Letters, Vol. 9, No. 3, (1999), pp. 293-300.https://doi.org/10.1023/A:1018628609742.

      [30] Khandoker AH, Lai DTH, Begg RK, &Palaniswami M, “Wavelet based feature extraction for support vector machines for screening balance impairments in the elderly”, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 15, No. 4, (2007), pp. 587-597https://doi.org/10.1109/TNSRE.2007.906961.

      [31] SahuOmkishor, Anand V, KanhangadVivek&Pachori RB, “Classification of magnetic resonance brain images using bi-dimensional empirical mode decomposition and autoregressive model”, Biomed Eng Letter, Vol. 5, (2015), pp.311-320.https://doi.org/10.1007/s13534-015-0208-9.

      [32] Kohavi R, “A study of cross-validation and bootstrap for accuracy estimation and model selection”, in International Joint Conference on Artificial intelligence, Vol. 14, No. 2, (1995), pp. 1137–1145.


 

View

Download

Article ID: 14809
 
DOI: 10.14419/ijet.v7i4.14809




Copyright © 2012-2015 Science Publishing Corporation Inc. All rights reserved.