Application of higher-order spectra for automated grading of diabetic maculopathy

Medical and Biological Engineering and Computing

Volumn 53, Issue 12, 2015, Pages 1319-1331

  Mookiah, Muthu Rama Krishnan ^a   Acharya, U Rajendra ^a,b,c   Chandran, Vinod ^d   Martis, Roshan Joy ^e   Tan, Jen Hong ^a   Koh, Joel E W ^a   Chua, Chua Kuang ^a   Tong, Louis ^f,g,h,i   Laude, Augustinus ^j  

^a DEPARTMENT OF ELECTRONICS AND COMPUTER ENGINEERING   (Singapore)

^b SINGAPORE UNIVERSITY OF SOCIAL SCIENCES   (Singapore)

^c UNIVERSITY OF MALAYA   (Malaysia)

^d QUEENSLAND UNIVERSITY OF TECHNOLOGY   (Australia)

^e ST JOSEPH ENGINEERING COLLEGE   (India)

^f SINGAPORE EYE RESEARCH INSTITUTE   (Singapore)

^g SINGAPORE NATIONAL EYE CENTRE   (Singapore)

^h DUKE NUS MEDICAL SCHOOL   (Singapore)

ⁱ NATIONAL UNIVERSITY OF SINGAPORE   (Singapore)

^j TAN TOCK SENG HOSPITAL   (Singapore)

more..

Author keywords

Computer aided diagnosis; Diabetic maculopathy; Higher order spectra; Retina; Spectral regression discriminant analysis

Indexed keywords

ADAPTIVE BOOSTING; COMPUTER AIDED ANALYSIS; DIAGNOSIS; DISCRIMINANT ANALYSIS; GRADING;

CLINICALLY SIGNIFICANT MACULAR EDEMA; HIGHER ORDER SPECTRUM; MACULOPATHY; MANUAL EXAMINATION; MINIMUM REDUNDANCY-MAXIMUM RELEVANCES; RETINA; SPECTRAL REGRESSIONS; SUPERVISED CLASSIFIERS;

COMPUTER AIDED DIAGNOSIS;

AREA UNDER THE CURVE; ARTICLE; AUTOMATION; BAYESIAN LEARNING; CLASSIFICATION; DIABETIC MACULAR EDEMA; HIGHER ORDER SPECTRA; PRIORITY JOURNAL; SPECTROSCOPY; SUPPORT VECTOR MACHINE; ADULT; COMPUTER ASSISTED DIAGNOSIS; DIABETIC RETINOPATHY; HUMAN; MACULAR EDEMA; MIDDLE AGED; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; VISUAL SYSTEM EXAMINATION; YOUNG ADULT;

ADULT; DIABETIC RETINOPATHY; DIAGNOSTIC TECHNIQUES, OPHTHALMOLOGICAL; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MACULAR EDEMA; MIDDLE AGED; ROC CURVE; YOUNG ADULT;

EID: 84949089067     PISSN: 01400118     EISSN: 17410444     Source Type: Journal    
DOI: 10.1007/s11517-015-1278-7     Document Type: Article

References (51)

1. Adarsh P (2013) A novel approach for diagnosis and severity grading of diabetic maculopathy. In: 2013 international conference on advances in computing, communications and informatics (ICACCI), pp 1230–1235
2. Ahmad Fadzil MH, Izhar LI, Nugroho H, Nugroho HA (2011) Analysis of retinal fundus images for grading of diabetic retinopathy severity. Med Biol Eng Comput 49(6):693–700
3. Akram MU, Akhtar M, Javed MY (2012) An automated system for the grading of diabetic maculopathy in fundus images. In: Huang T, Zeng Z, Li C, Leung CS (eds) Neural information processing. Springer, pp 36–43
4. Ang MH, Acharya UR, Sree SV, Lim T-C, Suri JS (2011) Computer-based identification of diabetic maculopathy stages using fundus images. In: Multi modality state-of-the-art medical image segmentation and registration methodologies. Springer, pp 377–399
5. Bock R, Meier J, Nyúl LG, Hornegger J, Michelson G (2010) Glaucoma risk index: automated glaucoma detection from color fundus images. Med Image Anal 14(3):471–481
6. Cai D, He X, Han J (2008) Srda: an efficient algorithm for large-scale discriminant analysis. IEEE Trans Knowl Data Eng 20(1):1–12
7. Chandran V, Elgar SL (1992) Position, rotation, and scale invariant recognition of images using higher-order spectra. In: IEEE international conference on acoustics, speech, and signal processing (ICASSP92). 5:213
8. Chandran V, Elgar SL (1993) Pattern recognition using invariants defined from higher order spectra-one-dimensional inputs. IEEE Trans Signal Proces 41(1):205–212
9. Chandran V, Steve E (1991) Shape discrimination using invariants defined from higher-order spectra. In: International conference on acoustics, speech, and signal processing ICASSP91, pp 3105–3108
10. Chandran V, Carswell B, Boashash B, Elgar S (1997) Pattern recognition using invariants defined from higher order spectra: 2-d image inputs. IEEE Trans Image Proces 6(5):703–712
11. Chowriappa P, Dua S, Acharya UR, Mookiah MRK (2013) Ensemble selection for feature-based classification of diabetic maculopathy images. Comput Biol Med 43(12):2156–2162
12. Chua KC, Chandran V, Acharya UR, Ng EYK, Caroline C, Manjunath G, Lim CM, Yan JMT, Gracielynne F, Suri JS (2008a) Computer-based detection of diabetes maculopathy stages. In: Acharya UR, Ng EYK, Suri JS (eds) Image modelling of the human eye. Artech House Inc, Norwood
13. Chua KC, Chandran V, Acharya UR, Lim CM (2008b) Cardiac state diagnosis using higher order spectra of heart rate variability. J Med Eng Technol 32(2):145–155
14. Chua KC, Chandran V, Acharya UR, Lim CM (2009) Analysis of epileptic eeg signals using higher order spectra. J Med Eng Technol 33(1):42–50
15. Chua KC, Chandran V, Acharya UR, Lim CM (2011) Application of higher order spectra to identify epileptic EEG. J Med Syst 35(6):1563–1571
16. Chua CK, Mookiah MRK, Koh JEW, Acharya UR, Lim CM, Laude A, Ng EYK et al (2013) Automated diagnosis of maculopathy stages using texture features. Int J Integr Care 13:1–2
17. Deepak KS, Sivaswamy J (2012) Automatic assessment of macular edema from color retinal images. IEEE Trans Med Imaging 31(3):766–776
18. Diep TM, Tsui I (2013) Risk factors associated with diabetic macular edema. Diabetes Res Clin Pract 100(3):298–305
19. Duda RO, Hart PE, Stork DG (2012) Pattern classification. Wiley, New York
20. Fleming AD, Goatman KA, Philip S, Prescott GJ, Sharp PF, Olson JA (2010) Automated grading for diabetic retinopathy: a large-scale audit using arbitration by clinical experts. Br J Ophthalmol 94(12):1606–1610
21. Foracchia M, Ruggeri A (2004) Automatic estimation of endothelium cell density in donor corneas by means of fourier analysis. Med Biol Eng Comput 42(5):725–731
22. Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119–139
23. Geoff D, Johnson MJ, Wiers MD (2010) Measurement of retinal vascular tortuosity and its application to retinal pathologies. Med Biol Eng Comput 48(1):87–95
24. Gun AM, Gupta MK, Dasgupta B (2008) Fundamentals of statistics (Vol I & II), 4th edn. World Press Private Ltd, Kolkata
25. Harney F (2006) Diabetic retinopathy. Medicine 34(3):95–98. ISSN 1357–3039
26. Hunter A, Lowell JA, Ryder B, Basu A, Steel D (2011) Automated diagnosis of referable maculopathy in diabetic retinopathy screening. In: Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, pp. 3375–3378
27. International Diabetes Federation (2013) Idf diabetes atlas. http://www.idf.org/sites/default/files/EN_6E_Atlas_Full_0.pdf. Accessed 31 Sep 2014
28. Khademi A, Krishnan S (2007) Shift-invariant discrete wavelet transform analysis for retinal image classification. Med Biol Eng Comput 45(12):1211–1222
29. Khushaba RN, Sarath K, Sara L, Gamini D (2011) Driver drowsiness classification using fuzzy wavelet-packet-based feature-extraction algorithm. IEEE Trans Biomed Eng 58(1):121–131
30. Kiire CA, Porta M, Chong V (2013) Medical management for the prevention and treatment of diabetic macular edema. Surv Ophthalmol 58(5):459–465
31. Li H, Chutatape O (2004) Automated feature extraction in color retinal images by a model based approach. IEEE Trans Biomed Eng 51(2):246–254
32. Lim ST, Zaki WMDW, Hussain A, Lim SL, Kusalavan S (2011) Automatic classification of diabetic macular edema in digital fundus images. In: 2011 IEEE colloquium on humanities, science and engineering (CHUSER), pp. 265–269
33. Luca G, Fabrice M, Karnowski TP, Li Y, Garg S, Tobin KW, Chaum E (2012) Exudate-based diabetic macular edema detection in fundus images using publicly available datasets. Med Image Anal 16(1):216–226
34. Mendel JM (1991) Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications. Proceed IEEE 79(3):278–305
35. Mookiah MRK, Oliver F (2013) Automated glaucoma detection using hybrid feature extraction in retinal fundus images. J Mech Med Biol 13(01):1350011(1)–1350011(21)
36. Mookiah MRK, Acharya UR, Chua CK, Lim CM, Ng EYK, Laude A (2013a) Computer-aided diagnosis of diabetic retinopathy: a review. Comput Biol Med 43(12):2136–2155
37. Mookiah MRK, Acharya UR, Martis RJ, Chua CK, Lim CM, Ng EYK, Laude A (2013b) Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: a hybrid feature extraction approach. Knowl Based Syst 39:9–22
38. Nayak J, Bhat PS, Acharya UR (2009) Automatic identification of diabetic maculopathy stages using fundus images. J Med Eng Technol 33(2):119–129
39. Neelakshi B, Grigorian RA, Tutela A, Zarbin MA (2009) Diabetic macular edema: pathogenesis and treatment. Survey Ophthalmol 54(1):1–32
40. Nikias CL, Mendel JM (1993) Signal processing with higher-order spectra. IEEE Signal Proces Mag 10(3):10–37
41. Noronha KP, Acharya UR, Nayak KP, Martis RJ, Bhandary SV (2014) Automated classification of glaucoma stages using higher order cumulant features. Biomed Signal Proces Control 10:174–183
42. Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238. ISSN 0162–8828
43. Pisano ED, Zong S, Hemminger BM, DeLuca M, Johnston RE, Muller K, Braeuning MP, Pizer SM (1998) Contrast limited adaptive histogram equalization image processing to improve the detection of simulated spiculations in dense mammograms. J Digital Imaging 11(4):193–200
44. Radon J (1986) On the determination of functions from their integral values along certain manifolds. IEEE Trans Med Imaging 5(4):170–176
45. Siddalingaswamy PC, Gopalakrishna PK (2010) Automatic grading of diabetic maculopathy severity levels. In: 2010 International conference on systems in medicine and biology (ICSMB), pp 331–334
46. Swami A, Mendel JM, Nikias CLM (1998) Higher-order spectral analysis toolbox: for use with MATLAB: user’s guide, vol 2. Mathworks Inc., pp 1–258
47. Tariq A, Akram MU, Shaukat A, Khan SA (2013) Automated detection and grading of diabetic maculopathy in digital retinal images. J Digit Imaging 26(4):803–812
48. ter Haar F (2005) Automatic localization of the optic disc in digital colour images of the human retina. PhD thesis, Citeseer
49. Vapnik V (2000) The nature of statistical learning theory. Springer, Berlin
50. Yau JWY, Rogers SL, Kawasaki R, Lamoureux EL, Kowalski JW, Bek T, Chen S-J, Dekker JM, Fletcher A, Grauslund J et al (2012) Global prevalence and major risk factors of diabetic retinopathy. Diabetes Care 35(3):556–564
51. Zhuo Z, Keong KC, Jiang L, Lui CCY, Tin A, Tien W (2012) Automatic glaucoma diagnosis with mRMR based feature selection. J Biom Biostat S7:1–8