Segmentation and texture analysis with multimodel inference for the automatic detection of exudates in early diabetic retinopathy


Diabetic retinopathy (DR) is an eye disease caused by the increase of insulin in blood and may cause blindness if not treated at an early stage. Exudates are the primary sign of DR. Currently there is no fully automated method to detect exudates in the literature and it would be useful in large scale screening if fully automatic method is available. In this paper we developed a novel method to detect exudates that based on interactions between texture analysis and segmentation with mathematical morphological technique by using multimodel inference. The texture analysis involves three components: they are statistical texture analysis, high order spectra analysis, and fractal analysis. The performance of the proposed method is assessed by the sensitivity, specificity and accuracy using the public data DIARETDB1. Our results show that the sensitivity, specificity and accuracy are 95.7%, 97.6% and 98.7% (SE = 0.01), respectively. It is shown that the proposed method can be run automatically and also improve the accuracy of exudates detection significantly over most of the previous methods.

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Lee, J. , Zee, B. and Li, Q. (2013) Segmentation and texture analysis with multimodel inference for the automatic detection of exudates in early diabetic retinopathy. Journal of Biomedical Science and Engineering, 6, 298-307. doi: 10.4236/jbise.2013.63038.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Fong, D.S. et al., (2003) Diabetic retinopathy. Diabetes Care, 26, 226-229. doi:10.2337/diacare.26.1.226
[2] Sopharak, A., Uyyanonvara, B., Barman, S. and Williason, T. (2010) Comparative analysis of automatic exudate detection algorithms. WCE, London.
[3] Sánchez, C.I., et al. (2010) Improving hard exudates detection in retinal images through a combination of local and contextual information. 2010 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Rotterdam, 14-17 April 2010, 5-8.
[4] Kande, G., Subbaiah, P. and Savithri, T. (2008) Segmentation of exdates and optic disk in retinal images. IEEE Processing of 6th Indian Conference on Computer Vision, Graphic & Image, Bhubaneshwar, 16-19 December 2008.
[5] Welfer, D., et al. (2009) A coarse-to-fine strategy for automatically detecting exudates in color eye fundus images. Computerized Medical Imaging and Graphics, 34, 228-235.
[6] Ravishankar, S., Jain, A. and Mittal, A. (2009) Automated feature extraction for early detection of diabetic retinopathy in fundus images. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Miami, 20-25 June 2009.
[7] Garcia, M., et al. (2009) Neural network based detection of hard exudates in retinal images. Computer Methods and Programs in Biomedicine, 93, 9-19. doi:10.1016/j.cmpb.2008.07.006
[8] Jaafar, H., Nandi, A. and Al-Nuaimy, W. (2010) Automated detection of exudates in retinal images using a split and merge algorithm. EUSIPCO, Aalborg, 23-27 August 2010.
[9] Sanchez, C.I., et al. (2008) A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis. Medical Engineering & Physics, 30, 350-357. doi:10.1016/j.medengphy.2007.04.010
[10] Sopharak, A., et al. (2009) Machine learning approach to automatic exudate detection in retinal images from diabetic retinopathy. Journal of Modern Optics, 57, 1-12.
[11] Akram, M.U., et al. (2012) Automated detection of exu-dates in colored retinal images for diagnosis of diabetic retinopathy. Applied Optics, 51, 4858-4866.
[12] Gonzalez, R.C. and Woods, R.E. (2008) Digital image processing. 3rd Edition, Prentice Hall, Upper Saddle River.
[13] Hwang, H. and Haddad, R.A. (1995) Adoptive median filters: New algorithm and results. IEEE Transactions on Image Processing, 4, 499, doi:10.1109/83.370679
[14] Tanaka, G., Suetake, N. and Uchino, E. (2010) Image enhancement based on nonlinear smoothing and sharpening for noisy images. Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), 14, 200-207.
[15] Akram, M.U., Khan, A., Iqbal, K. and Butt, W.H. (2010) Retinal image: Optic disk localization and detection. Image Analysis and Recognition, Lecture Notes in Computer Science, 6112, 40-49. doi:10.1007/978-3-642-13775-4_5
[16] Otsu, N. (1979) A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9, 62-66. doi:10.1109/TSMC.1979.4310076
[17] Tan, J.H., Ng, E.Y.K. and Acharya, U.R. (2009) Study of normal ocular thermogram using textural parameters. Infrared Physics & Technology, 53, 120-126.
[18] Bremananth, R., Nithya, B. and Saipriya, R. (2009) Wood species recognition system using GLCM and correlation. International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom’ 09), Kottayam, 27-28 October 2009, 615-619.
[19] Bailey, R.R. and Srinath, M.D. (2002) Orthogonal moment features for use with parametric and non-parametric classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 18, 389-399.
[20] Silakari, S., Motwani, M. and Maheshwari, M. (2009) Color image clustering using block truncation algorithm. International Journal of Computer Science Issues, 4, 31-35.
[21] Press, W.H., Flannery, B.P., Teukolsky, S.A. and Vetterling, W.T. (1990) Numerical recipes in C: The art of scientific computing. Cambridge University Press, New York, 1990.
[22] Acharaya, U.R., Ng, E.Y.K., Tan, J.H., Sree, S.V. and Ng, K.H. (2012) An integrated index for the identification of diabetic retinopathy stages using texture parameters. Journal of Medical Systems, 36, 2011-2020.
[23] Tuceryan, M. and Jain, A.K. (1993) Texture analysis. In: Chen, C.H., Pau, L.F. and Wang, P.S.P., Eds., Handbook of Pattern Recognition & Computer Vision, World Scientific Pub Co Inc., Singapore City. doi:10.1142/9789814343138_0010
[24] Weszka, J.S. and Ro-senfield, A. (1976) An application of texture analysis to material inspection. Pattern Recognition, 8, 195-200. doi:10.1016/0031-3203(76)90039-X
[25] Galloway, M.M. (1975) Texture classification using gray level run length. Computer Graphics and Image Processing, 4, 172-179. doi:10.1016/S0146-664X(75)80008-6
[26] Acharya, U.R., Chua, K.C., Ng, E.Y.K., Wei, W. and Chee, C. (2008) Application of higher order spectra for the identification of diabetes retinopathy stages. Journal of Medical Systems, 32, 431-488.
[27] Hubbard, L.D., Brothers, R.J., King, W.N., Clegg, L.X., Klein, R., Cooper, L.S., Sharrett, A.R., Davis, M.D. and Cai, J. (1999) Methods for evaluation of retinal micro-vascular abnormalities associated with hypertension/ sclerosis in the atherosclerosis risk in communities study. Ophthalmology, 106, 2269-2280. doi:10.1016/S0161-6420(99)90525-0
[28] Azemin, M.Z.C., Kumar, D.K., Wong, T.Y., Wang, J.J., Kawasaki, R. and Mit-chell, P. (2010) Retinal stroke prediction using logistic-based fusion of multiscale fractal analysis. 2010 IEEE International Conference on Imaging Systems and Techniques (IST), Thes-saloniki, 1-2 July 2010, 125-128.
[29] Azemin, M.Z.C., Kumar, D.K., Wong, T.Y., Wang, J.J., Kawasaki, R., Mitchell, P. and Arjunan, S.P. (2010) Fusion of multiscale wavelet-based fractal analysis on retina image for stroke prediction. 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Buenos Aires, 31 August 2010-4 September 2010, 4308-4311,
[30] Azemin, M.Z.C., Kumar, D.K., Wong, T.Y., Kawasaki, R., Mitchell, P. and Wang, J.J. (2011) Robust methodology for fractal analysis of the retinal vasculature. IEEE Transactions on Medical Imaging, 30.
[31] Stosic, T. and Stosic, B.D. (2006) Multifractal analysis of human retinal vessels. IEEE Transactions on Medical Imaging, 25, 1101-1107. doi:10.1109/TMI.2006.879316
[32] MacGillivary, T.J., Patton, N., Doubal, F.N., Graham, C. and Wardlaw, J.M. (2007) Fractal analysis of the retinal vascular network in fundus images. Proceedings of the 29th Annual International Conference of the IEEE EMBS, Lyon, 23-26 August 2007
[33] Andjelkovic, J., Zivic, N., Reljin, B., Celebic, V. and Salom I. (2008) Application of multifractal analysis on medical images. Wseas Transactions on Information Science and Applications, 5.
[34] Faust, O., Acharya, R., Ng, E.Y.K., Ng, K.H. and Suri, J.S. (2012) Algorithm for the automated detection of diabetic retinopathy using digital fundus images: A review. Journal of Medical Systems, 36, 145-157.
[35] Marcel, D. and Peter, B. (2003) Supervised gene clustering with penalized logistic regression. Research Report No.115, Switzerland, 2003
[36] Anderson, D.R. (2008) Model based inference in the life sciences. Springer-Verlag, New York. doi:10.1007/978-0-387-74075-1
[37] Barton, K. (2009) Mu-MIn: Multi-model inference. R Package Version 0.12.2/r18.
[38] Buckland, S.T., Burnham, K.P. and Augustin, N.H. (1997) Model selection: An integral part of inference. Biometrics, 53, 603-618. doi:10.2307/2533961
[39] Burnham, K.P. and Anderson, D.R. (2002) Model selection and multimodel inference. Springer-Verlag, New York.
[40] Haykin, S. (1999) Neural networks: A comprehensive foundation. Prentice-Hall International, New Jersey.
[41] Bishop, C.M. (2004) Neural networks for pattern recognition. Oxford University Press, New York.
[42] Hornik, K. (1991) Approximation capabilities of multilayer feedforward networks. Neural Networks, 4, 251-257. doi:10.1016/0893-6080(91)90009-T
[43] Huang, G.B., Chen, Y.Q. and Babri, H.A.(2000) Classification ability of single hidden layer feedforward neural networks. IEEE Transactions on Neural Networks, 11, 799-801. doi:10.1109/72.846750
[44] DIARETDB1 database (2007).

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