Decision support system for the glaucoma using Gabor transformation

Biomedical Signal Processing and Control

Volumn 15, Issue , 2015, Pages 18-26

  Acharya, U Rajendra ^a,b   Ng, E Y K ^c   Eugene, Lim Wei Jie ^a   Noronha, Kevin P ^d   Min, Lim Choo ^a   Nayak, K Prabhakar ^d   Bhandary, Sulatha V ^e  

^a DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING   (Singapore)

^b UNIVERSITY OF MALAYA   (Malaysia)

^c NANYANG TECHNOLOGICAL UNIVERSITY   (Singapore)

^d MANIPAL INSTITUTE OF TECHNOLOGY   (India)

^e KASTURBA MEDICAL COLLEGE   (India)

Author keywords

[No Author keywords available]

Indexed keywords

DIAGNOSIS; ENTROPY; HIGHER ORDER STATISTICS; OPHTHALMOLOGY; PATIENT TREATMENT; SUPPORT VECTOR MACHINES;

DIAGNOSIS METHODS; DIGITAL FUNDUS IMAGES; GABOR TRANSFORM; INTRA OCULAR PRESSURE; PRINCIPAL COMPONENTS; RANKING METHODS; RECEIVER OPERATING CURVES; SPACE ALGORITHMS;

DECISION SUPPORT SYSTEMS;

ALGORITHM; ARTICLE; CLINICAL FEATURE; CONTROLLED STUDY; DECISION SUPPORT SYSTEM; ENTROPY; GABOR TRANSFORMATION; GLAUCOMA; HUMAN; MACHINE LEARNING; PRINCIPAL COMPONENT ANALYSIS; PRIORITY JOURNAL; RANK SUM TEST; STUDENT T TEST; SUPPORT VECTOR MACHINE;

EID: 84940572269     PISSN: 17468094     EISSN: 17468108     Source Type: Journal    
DOI: 10.1016/j.bspc.2014.09.004     Document Type: Article

References (64)

1. R. Lim, and I. Goldberg The Glaucoma Book: A Practical, Evidence-Based Approach to Patient Care 2010 (Chapter 1, p. 3)
2. T.C. Lim, S. Chattopadhyay, and U.R. Acharya A survey and comparative study on the instruments for glaucoma detection Med. Eng. Phys. 34 2 2012 129 139
3. R.R.A. Bourne The optic nerve head in glaucoma Commun. Eye Health 19 59 2006 44 45
4. A. Sommer, J.M. Tielsch, J. Katz, H.A. Quigley, J.D. Gottsch, J. Javitt, and K. Singh Relationship between intraocular pressure and primary open angle glaucoma among white and black Americans. The Baltimore Eye Survey Arch. Ophthalmol. 109 8 1991 1090 1095
5. H.A. Quigley, and A.T. Broman The number of people with glaucoma worldwide in 2010 and 2020 Br. J. Ophthalmol. 90 3 2006 262 267
6. C.F. Burgoyne Pearls of Glaucoma Management 2010 Springer Berlin, Heidelberg (Chapter 1. p. 1)
7. Eye Diseases Prevalence Research Group Prevalence of open-angle glaucoma among adults in the United States Arch. Ophthalmol. 122 2004 532 538
8. H. Goldmann, and T. Schmidt Applanation Tonometry Ophthalmologica 134 1957 221 242
9. J.P. Nordmann, M. Mesbah, and G. Berdeaux Scoring of visual field measured through humphrey perimetry: Principal component varimax rotation followed by validated cluster analysis Invest. Ophthalmol. Vis. Sci. 46 2005 3169 3176
10. P. Sharma, P.A. Sample, L.M. Zangwill, and J.S. Schuman Diagnostic tools for glaucoma detection and management Surv. Ophthalmol. 53 1 2008 S17 S32
11. L.A. Yannuzzi, M.D. Ober, J.S. Slakter, R.F. Spaide, Y.L. Fisher, R.W. Flower, and R. Rosen Ophthalmic fundus imaging: today and beyond Am. J. Ophthalmol. Vol. 137 3 2004 511 524
12. R. Acharya, W.L. Yun, E.Y.K. Ng, W. Yu, and J.S. Suri Imaging systems of human eye: a review J. Med. Syst. 32 4 2008 301 315
13. K. Noronha, U.R. Acharya, K.P. Nayak, S. Kamath, and S.V. Bhandary Decision support system for diabetic retinopathy using discrete wavelet transform Proc. Inst. Mech. Engrs. Part H: J. Eng. Med. 227 3 2013 251 261
14. U.R. Acharya, C.K. Chua, E.Y.K. Ng, W. Yu, and C. Chee Application of higher order spectra for the identification of diabetes retinopathy stages J. Med. Syst. 32 6 2008 481 488
15. U.R. Acharya, C.M. Lim, E.Y.K. Ng, C. Chee, and T. Tamura Computer-based detection of diabetes retinopathy stages using digital fundus images Proc. Inst. Mech. Engrs. Part H: J. Eng. Med. 223 5 2009 545 553
16. W.L. Yun, U.R. Acharya, Y.V. Venkatesh, C. Chee, C.M. Lim, and E.Y.K. Ng Identification of different stages of diabetic retinopathy using retinal optical images Int. Sci. 178 1 2008 106 121
17. R. Chrástek, M. Wolf, K. Donath, H. Niemann, D. Paulus, T. Hothorn, B. Lausen, R. Lämmer, C. Mardin, and G. Michelson Automated segmentation of the optic nerve head for diagnosis of glaucoma Med. Image Anal. 9 4 2005 297 314
18. P. Betz, F. Camps, J. Collignon-Brach, G. Lavergne, and R. Weekers Biometric study of the disc cup in open-angle glaucoma GraefesArch. Clin. Exp. Ophthalmol. 218 2 1982 70 74
19. D. Kourkoutas, I.S. Karanasiou, G.J. Tsekouras, M. Moshos, E. Iliakis, and G. Georgopoulos Glaucoma risk assessment using a non-linear multivariable regression method Comput. Methods Programs Biomed. 108 3 2012 31149 31159
20. J. Xu, O. Chutatape, E. Sung, C. Zheng, and P.C.T. Kuan Optic disk feature extraction via modified deformable model technique for glaucoma analysis Pattern Recog. 40 7 2007 2063 2076
21. J.J. Kanski, J.A. McAllister, and J.F. Salmon Glaucoma: A Colour Manual of Diagnosis and Treatment 2nd ed. 1996 Butterworth-Heinemann Oxford; Boston
22. Glaucoma Guide, 2010. Available: http://www.medrounds.org/glaucoma-guide/2006/02/section-1-b-meaning-of-cupping.html
23. A.J. Vingrys The many faces of glaucomatous optic neuropathy Clin. Exp. Optomet. 83 2000 145 160
24. R.M. Nishikawa, M.L. Giger, K. Doi, C.J. Vyborny, and R.A. Schmidt Computer-aided detection of clustered micro calcifications on digital mammograms Med. Biol. Eng. Comput. 33 2 1995 174 178
25. L.G. Nyúl Retinal image analysis for automated glaucoma risk evaluation Sixth International Symposium on Multispectral Image Processing and Pattern Recognition 2009 International Society for Optics and Photonics 74971C
26. M. Lalonde, M. Beaulieu, and L. Gagnon Fast and robust optic disc detection using pyramidal decomposition and Hausdorff-based template matching IEEE Trans. Med. Imag. 20 11 2011 1193 1200
27. A. Hoover, and M. Goldbaum Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels IEEE Trans. Biomed. Eng. 22 8 2003 951 958
28. J. Xu, O. Chutatape, and P. Chew Automated optic disk boundary detection by modified active contour model IEEE Trans. Biomed. Eng. 54 3 2007 473 482
29. M. Foracchia, E. Grisan, and A. Ruggeri Detection of optic disc in retinal images by means of a geometrical model of vessel structure IEEE Trans. Biomed. Eng. 23 10 2004 1189 1195
30. A.D. Fleming, K.A. Goatman, S. Philip, J.A. Olson, and P.F. Sharp Automatic detection of retinal anatomy to assist diabetic retinopathy screening Phys. Med. Biol. 52 2 2007 331 345
31. G. Joshi, J. Sivaswamy, and S.R. Krishnadas Depth discontinuity-based cup segmentation from multiview color retinal images IEEE Trans. Biomed. Eng. 59 6 2012 1523 1531
32. J. Nayak, U.R. Acharya, P.S. Bhat, A. Shetty, and T.C. Lim Automated diagnosis of glaucoma using digital fundus images J. Med. Syst. 33 5 2009 337 346
33. Yanwu Xu, and et al. Sliding window and regression based cup detection in digital fundus images for glaucoma diagnosis Medical Image Computing and Computer-Assisted Intervention - MICCAI 2011 2011 Springer Berlin, Heidelberg 1 8
34. H. Yuji, A. Noudo, C. Muramatsu, A. Sawada, T. Hara, T. Yamamoto, and H. Fujita Vertical cup-to-disc ratio measurement for diagnosis of glaucoma on fundus images SPIE Medical Imaging 2010 International Society for Optics and Photonics 76243C
35. R.C. Gonzalez, and R.E. Woods Digital Image Processing 2nd ed. 2001 Prentice Hall New Jersey
36. J.G. Daugman Complete discrete 2-D Gabor transforms by neural networks for image analysis and compression IEEE Trans. Acoust. Speech Sig. Process. 36 7 1988 1169 1179
37. D.A. Clausi, and M.E. Jernigan Designing Gabor filters for optimal texture separability Pattern Recog. 33 11 2000 1835 1849
38. A.K. Jain, and F. Farrokhnia Unsupervised texture segmentation using Gabor Filters Pattern Recog. 24 12 1991 1167 1186
39. D. Zheng, Y. Zhao, and J. Wang Features extraction using a Gabor filter family Proceedings of the Sixth Lasted International Conference, Signal and Image Processing Hawaii 2004
40. J.H. Tan, E.Y.K. Ng, U.R. Acharya, and C. Chee Study of normal ocular thermogram using textural parameters Infrared Phys. Technol. 53 2 2010 120 126
41. A.P.S. Pharwaha, and B. Singh Shannon and non-Shannon measures of entropy for statistical texture feature extraction in digitized mammograms Proceedings of the World Congress on Engineering and Computer Science, vol. 2 2009 20 22
42. M.R.K. Mookiah, U.R. Acharya, R.J. Martis, C.K. Chua, C.M. Lim, E.Y.K. Ng, and A. Laude Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: a hybrid feature extraction approach Knowledge-Based Syst. 39 2013 9 22
43. B. Moore Principal component analysis in linear systems: controllability, observability, and model reduction IEEE Trans. Automat. Cont. 26 1 1981 17 32
44. M. Al-Naser, and U. Soderstrom Reconstruction of occluded facial images using asymmetrical principal component analysis Integr. Comput.-Aided Eng. 19 3 2012 273 283
45. A.M. Goon, M.K. Gupta, and B. Dasgupta An Outline of Statistical Theory 1973 World Press Private
46. R.O. Duda, P.E. Hart, and D.G. Stork Pattern Classification 2012 John Wiley and Sons New York
47. J.F. Box Guinness, Gosset, Fisher, and small samples Stat. Sci. 2 1987 45 52
48. Huiqing Liu, Jinyan Li, and Limsoon Wong A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns Genome Informatics Series 2002 51 60
49. V. Vapnik The Nature of Statistical Learning Theory 1995 Springer-Verlag New York
50. C.J.C. Burgess A tutorial on support vector machines for pattern recognition Data Min. Knowledge Discov. 2 2 1998 1 47
51. D. Heckerman, D. Geiger, and D.M. Chickering Learning Bayesian networks: the combination of knowledge and statistical data Mach. Learn. 20 3 1995 197 243
52. R.R. Yager An extension of the naive Bayesian classifier Inform. Sci. 176 2006 577 588
53. K.C. Chua, V. Chandran, U.R. Acharya, and C.M. Lim Application of higher order statistics/spectra in biomedical signals - A review Med. Eng. Phys. 32 7 2010 679 689
54. K.A. Townsend, G. Wollstein, D. Danks, K.R. Sung, H. Ishikawa, L. Kagemann, M.L. Gabriele, and J.S. Schuman Heidelberg Retina Tomograph 3 machine learning classifiers for glaucoma detection Br. J. Ophthalmol. 92 2008 814 818
55. F. Fink, K. Worle, P. Gruber, A.M. Tome, J.M. Gorriz-Saez, C.G. Puntonet, and E.W. Lang ICA analysis of retina images for glaucoma classification Engineering in Medicine and Biology Society, EMBS 2008. 30th Annual International Conference of the IEEE 2008 4664 4667
56. R. Nagarajan, C. Balachandran, D. Gunaratnam, A. Klistorner, and S. Graham Neural network model for early detection of glaucoma using multi-focal visual evoked potential (M-Vep) Invest. Ophthalmol. Vis. Sci. 2002 42
57. R. Bock, J. Meier, L.G. Nýul, J. Hornegger, and G. Michelson Glaucoma risk index: automated glaucoma detection from color fundus images Med. Image Anal. 14 2010 471 481
58. M.L. Huang, H.Y. Chen, W.C. Huang, and Y.Y. Tsai Linear discriminant analysis and artificial neural network for glaucoma diagnosis using scanning laser polarimetry - variable cornea compensation measurements in Taiwan Chinese population Graefe's Arch. Clin. Exp. Ophthalmol. 248 3 2010 435 441
59. M. Balasubramanian, S. Zabic, C. Bowd, H.W. Thompson, P. Wolenski, S.S. Iyengar, B.B. Karki, and L.M. Zangwill Clinical evaluation of the proper orthogonal decomposition framework for detecting glaucomatous changes in human subjects Invest. Ophthalmol. Vis. Sci. 51 2010 264 271
60. R. Kolar, and J. Jan Detection of glaucomatous eye via color fundus images using fractal dimensions Radio Eng. 17 3 2008 109 114
61. U.R. Acharya, S. Dua, X. Du, S.V. Sree, and C.K. Chua Automated diagnosis of glaucoma using texture and higher order spectra features IEEE Trans. Inf. Technol. Biomed. 15 3 2011 449 455
62. S. Dua, U.R. Acharya, P. Chowriappa, and S.V. Sree Wavelet based energy features for glaucomatous image classification IEEE Trans. Inf. Technol. Biomed. 16 1 2012 80 87
63. M.R.K. Mookiah, U.R. Acharya, C.M. Lim, A. Petznick, and J.S. Suri Data mining technique for automated diagnosis of glaucoma using higher order spectra and wavelet energy features Knowledge-Based Syst. 33 2012 73 82
64. K.P. Noronha, U.R. Acharya, K.P. Nayak, R.J. Martis, and S.V. Bhandary Automated classification of glaucoma stages using higher order cumulant features Biomed. Sig. Process. Cont. 10 2014 174 183