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Computer-aided diabetic retinopathy detection using trace transforms on digital fundus images

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Abstract

Diabetic retinopathy (DR) is a leading cause of vision loss among diabetic patients in developed countries. Early detection of occurrence of DR can greatly help in effective treatment. Unfortunately, symptoms of DR do not show up till an advanced stage. To counter this, regular screening for DR is essential in diabetic patients. Due to lack of enough skilled medical professionals, this task can become tedious as the number of images to be screened becomes high with regular screening of diabetic patients. An automated DR screening system can help in early diagnosis without the need for a large number of medical professionals. To improve detection, several pattern recognition techniques are being developed. In our study, we used trace transforms to model a human visual system which would replicate the way a human observer views an image. To classify features extracted using this technique, we used support vector machine (SVM) with quadratic, polynomial, radial basis function kernels and probabilistic neural network (PNN). Genetic algorithm (GA) was used to fine tune classification parameters. We obtained an accuracy of 99.41 and 99.12 % with PNN–GA and SVM quadratic kernels, respectively.

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References

  1. Akram MU, Khalid S, Khan SA (2013) Identification and classification of microaneurysms for early detection of diabetic retinopathy, Pattern Recognition, 46(1), 2013. ISSN 107–116:0031–3203. doi:10.1016/j.patcog.2012.07.002

  2. Bob Zhang, Fakhri Karray, Qin Li, Lei Zhang (2012) Sparse representation classifier for microaneurysm detection and retinal blood vessel extraction. Inf Sci, Volume 200, 1 Oct 2012, Pages 78–90, ISSN 0020–0255. doi:10.1016/j.ins.2012.03.003

  3. Clara IS, María G, Agustín M, María IL, Roberto H (2009) Retinal image analysis based on mixture models to detect hard exudates. Med Image Anal, 13(4): 650–658, ISSN 1361–8415. doi:10.1016/j.media.2009.05.005

  4. Duda RO, Hart PE, Stork DG (2001) Pattern classification, 2nd edn. Wiley, USA

    Google Scholar 

  5. Fleming AD, Philip S, Goatman KA, Sharp PF, Olson JA (2012) Automated clarity assessment of retinal images using regionally based structural and statistical measures, Med Eng Phys, 34(7), 2012. ISSN 849–859:1350–4533. doi:10.1016/j.medengphy.2011.09.027

  6. Ganesan K, Acharya UR, Chua CK, Lim CM, Abraham KT One-class classification of mammograms using trace transform functionals, instrumentation and measurement. IEEE Trans, vol.PP, no.99, pp. 1,1, 0, doi:10.1109/TIM.2013.2278562

  7. García M, López MI, Álvarez D, Hornero R (2010) Assessment of four neural network based classifiers to automatically detect red lesions in retinal images. Med Eng Phys, 32(10). ISSN 1085–1093:1350–4533. doi:10.1016/j.medengphy.2010.07.014

  8. García M, Sánchez CI, López MI, Abásolo D, Hornero R (2009) Neural network based detection of hard exudates in retinal images. Comput Method Prog Biomed, 93(1). ISSN 9–19:0169–2607. doi:10.1016/j.cmpb.2008.07.006

  9. Gen M, Cheng R (1999) Genetic algorithms and engineering optimization. Vol. 7. Wiley-interscience

  10. Giancardo L, Meriaudeau F, Karnowski TP, Li Y, Garg S, Tobin KW, Jr., Edward C (2012) Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Med Image Analysis, 16(1). ISSN 216–226:1361–8415. doi:10.1016/j.media.2011.07.004

  11. Giancardo L, Meriaudeau F, Karnowski TP, Li Y, Tobin KW, Chaum E (2011) Microaneurysm detection with radon transform-based classification on retina images, Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, vol., no., pp. 5939,5942, 30 Aug 2011–3 Sept 2011. doi:10.1109/IEMBS.2011.6091562

  12. Goldberg DE (1989) Genetic algorithms in search, optimization, and machine learning. Addison-Wesley Press, Cambridge

    Google Scholar 

  13. http://messidor.crihan.fr/index-en.php

  14. Kadyrov A , Petrou M The trace transform and its applications, School of Electronic Engineering, Informational Technology and Mathematics, University of Surrey, Guildford, GU27XH, UK

  15. Kavitha D, Devi SS (2005) Automatic detection of optic disk and exudates in retinal images. In: Proceedings of the International Conference in Intelligent Sensing and Information Processing, Chennai, pp. 501–506

  16. Köse Cemal, ŞU, İkibaş C, Erdöl H (2012) Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images. Comput Method Prog Biomed, 107(2). ISSN 274–293:0169–2607. doi:10.1016/j.cmpb.2011.06.007

  17. Mookiah MRK, Rajendra Acharya U, Martis RJ, Chua CK, Lim CM, Ng EYK, Laude A (2013) Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: a hybrid feature extraction approach, knowledge-based systems. 39, February 2013, pp. 9–22, ISSN 0950–7051, http://dx.doi.org/10.1016/j.knosys.2012.09.008

  18. Muller KR, Mika S, Ratsch G, Tsuda K, Scholkopf B (2001) An introduction to kernel based learning algorithms. IEEE Trans Neural Netw 12(2):181–201

    Article  CAS  PubMed  Google Scholar 

  19. Niemeijer M, Abràmoff MD, van Ginneken B (2006) Image structure clustering for image quality verification of color retina images in diabetic retinopathy screening. Med Image Analysis, 10(6). ISSN 888–898:1361–8415. doi:10.1016/j.media.2006.09.006

  20. Noronha K, Acharya UR, Kamath S, Bhandary SV, Nayak KP (2012) Decision support system for diabetic retinopathy using discrete wavelet transform, Proceedings of the Institution of Mechanical Engineers, Part H: J Eng Med, 227(3)

  21. Patton N, Aslam TM, MacGillivray T, Deary IJ, Dhillon B, Eikelboom RH, Yogesan K, Constable IJ (2006) Retinal image analysis: concepts, applications and potential. Prog Retin Eye Res 25:99–127

    Article  PubMed  Google Scholar 

  22. Philips R, Forrester J, Sharp P (1993) Automated detection and quantification of retinal exudates. Graefes Arch Clin Exp Ophthalmol 231(2):90–94

    Article  Google Scholar 

  23. Quellec G, Lamard M, Abràmoff MD, Decencière E, Lay B, Erginay A, Cochener B, Cazuguel G (2012) A multiple-instance learning framework for diabetic retinopathy screening. Med Image Analysis, 16(6), 2012. ISSN 1228–1240:1361–8415. doi:10.1016/j.media.2012.06.003

  24. Qureshi RJ, Kovacs L, Harangi B, Nagy B, Peto T, Hajdu A (2012) Combining algorithms for automatic detection of optic disc and macula in fundus images. Comput Vis Image Underst 116(1). ISSN 138–145:1077–3142. doi:10.1016/j.cviu.2011.09.001

  25. Saleh MD, Eswaran C (2012) An automated decision-support system for non-proliferative diabetic retinopathy disease based on MAs and HAs detection, Comput Method Prog Biomed 108(1). ISSN 186–196:0169–2607. doi:10.1016/j.cmpb.2012.03.004

  26. Sánchez CI, Roberto H, López MI, Aboy M, Poza J, Abásolo D (2008) A novel automatic image processing algorithm for detection of hard exudates based on retinal image analysis. Med Eng Phys, 30(3), 2008. ISSN 350–357:1350–4533. doi:10.1016/j.medengphy.2007.04.010

  27. Singer DE, Nathan DM, Fogel HA, Schachat AP (1992) Screening for diabetic retinopathy. Ann Int Med 116(8):660–671

    Article  CAS  PubMed  Google Scholar 

  28. Wang J, Downs T (2003) Tuning pattern classifier parameters using a genetic algorithm with an application in mobile robotics, evolutionary computation, 2003. CEC ’03. The 2003 Congress on, 1: 581, 586 Vol. 1, 8–12 Dec. 2003 doi:10.1109/CEC.2003.1299628

  29. Webb AR (2002) Statistical pattern recognition. 2nd edition, Wiley, ISBN: 0470845147

  30. Winder RJ, Morrow PJ, McRitchie IN, Bailie JR, Hart PM (2009) Algorithms for digital image processing in diabetic retinopathy. Comput Med Imaging Gr 33(8). ISSN 608–622:0895–6111. doi:10.1016/j.compmedimag.2009.06.003

  31. World Health Organisation (2005) Prevention of blindness from diabetes mellitus: report of a WHO consultation in Geneva, Switzerland. WHO Library Cataloguing-in-Publication Data, Switzerland

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Acknowledgments

The study was funded by the NHG CSCS/12006 grant.

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Authors and Affiliations

  1. Department of ECE, Ngee Ann Polytechnic, Clementi Road, Clementi, 599489, Singapore

    Karthikeyan Ganesan, Roshan Joy Martis, U. Rajendra Acharya, Chua Kuang Chua & Lim Choo Min

  2. School of Mechanical and Aerospace Engineering, Nanyang Technological University, Nanyang, 639798, Singapore

    E. Y. K. Ng

  3. National Healthcare Group Eye Institute, Tan Tock Seng Hospital, Novena, 308433, Singapore

    Augustinus Laude

  4. Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia

    U. Rajendra Acharya

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  1. Karthikeyan Ganesan
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Correspondence to Karthikeyan Ganesan.

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Ganesan, K., Martis, R.J., Acharya, U.R. et al. Computer-aided diabetic retinopathy detection using trace transforms on digital fundus images. Med Biol Eng Comput 52, 663–672 (2014). https://doi.org/10.1007/s11517-014-1167-5

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  • DOI: https://doi.org/10.1007/s11517-014-1167-5

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