Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy

IEEE Transactions on Biomedical Engineering

Volumn 53, Issue 6, 2006, Pages 1084-1098

  Narasimha Iyer, Harihar ^a,e   Can, Ali ^b,f,g   Roysam, Badrinath ^a,h,i,j,k   Stewart, Charles V ^c   Tanenbaum, Howard L ^d,l,m,n,o,p   Majerovics, Anna ^d,m,q,r   Singh, Hanumant ^b,r  

^a RENSSELAER POLYTECHNIC INSTITUTE   (United States)

^b WOODS HOLE OCEANOGRAPHIC INSTITUTION   (United States)

^c Rensselaer Polytechnic Institute   (United States)

^d Center for Sight   (United States)

^e Association for Research in Vision and Ophthalmology   (United States)

^f GE GLOBAL RESEARCH   (United States)

^g Microscopy Society of America   (United States)

^h Electrical Computer and Systems Engineering Department   (United States)

ⁱ Center for Subsurface Sensing and Imaging Systems (CenSSIS)   (United States)

^j Biomedical Engineering Department   (United States)

^k ASEE   (United States)

^l MCGILL UNIVERSITY   (Canada)

^m ALBANY MEDICAL COLLEGE   (United States)

ⁿ Center for Sight in Albany

^o Royal College of Physician and Surgeons of Canada   (Canada)

^p CMAJ   (Canada)

^q Royal College of Surgeons of Canada   (Canada)

^r Northeast Eye Ear Nose and Throat Society of New York ^*  (United States)

more..

Author keywords

Bayesian classification; Change analysis; Change detection; Diabetic retinopathy; Illumination correction; Retinal image analysis

Indexed keywords

BAYESIAN CLASSIFICATION; CHANGE ANALYSIS; CHANGE DETECTION; DIABETIC RETINOPATHY; ILLUMINATION CORRECTION; RETINAL IMAGE ANALYSIS;

DISEASE CONTROL; ERROR ANALYSIS; IMAGE ANALYSIS; IMAGE SEGMENTATION; MEDICAL IMAGING; PATIENT MONITORING;

DISEASES;

ACCURACY; ALGORITHM; ANALYTICAL ERROR; ARTICLE; ARTIFACT; BAYES THEOREM; DIABETIC RETINOPATHY; EYE FUNDUS; FALSE POSITIVE RESULT; HUMAN; ILLUMINATION; IMAGE ANALYSIS; IMAGING SYSTEM; INFORMATION PROCESSING; RETINA;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; COLOR; COLORIMETRY; DIABETIC RETINOPATHY; FLUORESCEIN ANGIOGRAPHY; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; RETINA; SENSITIVITY AND SPECIFICITY;

EID: 33744986261     PISSN: 00189294     EISSN: None     Source Type: Journal    
DOI: 10.1109/TBME.2005.863971     Document Type: Article

References (68)

1. J. Evans, C. Rooney, S. Ashgood, N. Dattan, and R. Wormald, "Blindness and partial sight in England and Wales April 1900-March 1991," Health Trends, vol. 28, pp. 5-12, 1996.
2. M. J. Cree, J. A. Olson, K. C. McHardy, P. F. Sharp, and J. V. Forrester, "The preprocessing of retinal images for the detection of fluorescein leakage," Phys. Med. Biol., vol. 44, pp. 293-308, 1999.
3. _, "A fully automated comparative microaneurysm digital detection system," Eye, vol. 11, pp. 622-628, 1998.
4. K. A. Goatman, M. J. Cree, J. A. Olson, J. V. Forrester, and P. F. Sharp, "Automated measurement of m microaneurysm turnover," Investiga. Ophthalmol. Vis. Sci., vol. 44, pp. 5335-5341, 2003.
5. Z. B. Sbeh, L. D. Cohen, G. Mimoun, and G. Coscas, "A new approach of geodesic reconstruction for drusen segmentation in eye fundus images," IEEE Trans. Med. Imag., vol. 20, no. 12, pp. 1321-1333, Dec. 2001.
6. A. Osareh, M. Mirmehdi, B. Thomas, and R. Markham, "Classification and localization of diabetic-related eye disease," in Proc. 7th Eur. Conf. Computer Vision (ECCV), A. Heyden, G. Sparr, M. Nielsen, and P. Johansen, Eds., May 2002, pp. 502-516.
7. W. Hsu, P. M. D. S. Pallawala, M. L. Lee, and K. A. Eong, "The role of domain knowledge in the detection of retinal hard exudates," in Proc. IEEE CVPR, Hawaii, 2001.
8. R. Phillips, J. V. Forrester, and P. F. Sharp, "Automated detection and quantification of retinal exudates," Graefe's Arch. Clin. Exp. Ophthalmol., vol. 231, pp. 90-94, 1993.
9. H. Li and O. Chutatape, "Fundus image features extraction," in Proc. 22nd IEEE Int. Conf. IEEE Engineering in Medicine and Biology Society, 2000, vol. 4, pp. 3071-3073.
10. D. Usher, M. Dumsky, M. Himaga, T. H. Williamson, S. Nussey, and J. Boyce, "Automated detection of diabetic retinopathy in digital retinal images: a tool for diabetic retinopathy screening," Diabetic Med., vol. 21, no. 1, p. 84, January 2004.
11. T. Walter, J. C. Klein, P. Massin, and A. Erginay, "A contribution of image processing to the diagnosis of diabetic retinopathy detection of exudates in color fundus images of the human retina," IEEE Trans. Med. Imag., vol. 21, no. 10, pp. 1236-1243, Oct. 2002.
12. T. Spencer, J. A. Olson, K. C. McHardy, P. F. Sharp, and J. V. Forrester, "An image-processing strategy for the segmentation of microaneurysms in fluorescein angiograms of the ocular fundus," Comput. Biomed. Res., vol. 29, no. 4, pp. 284-302, Aug. 1996.
13. J. H. Hipwell, F. Strachan, J. A. Olson, K. C. McHardy, P. F. Sharp, and J. V. Forrester, "Automated detection of microaneurysms in digital red-free photographs: a diabetic retinopathy screening tool," Diabetic Med., vol. 17, no. 8, pp. 588-594, August 2000.
14. M. Niemeijer, B. van Ginneken, and M. D. Abramoff, "Automatic detection and classification of microaneurysms and small hemors in color fundus photographs," Eur. J. Ophthalmol., vol. 13, no. 2, p. 226, 2003.
15. R. T. Smith, T. Nagasaki, J. R. Sparrow, I. Barbazetto, C. C. W. Klaver, and J. K. Chan, "A method of drusen measurement based on the geometry of fundus reflectance," Biomed. Eng. Online, vol. 2, no. 1, p. 10, 2003.
16. L. Zhou, M. Rzeszotarski, L. J. Singerman, and J. M. Chokreff, "The detection and quantification of retinopathy using digital angiograms," IEEE Trans. Med. Imag., vol. 13, no. 4, pp. 619-626, Dec. 1994.
17. L. Ballerini, "An automatic system for the analysis of vascular lesions in retinal images," in Proc. IEEE Medical Imaging Conf., Seattle, Oct. 1999, pp. 1598-1602.
18. M. Goldbaum, S. Moezzi, A. Taylor, S. Chatterjee, J. Boyd, E. Hunter, and R. Jain, "Automated diagnosis and image understanding with object extraction, object classification, and inferencing in retinal images," in Proc. IEEE Int. Conf. Image Processing, 1996, vol. 3, pp. 695-698.
19. A. Pinz, S. Bernogger, P. Datlinger, and A. Kruger, "Mapping the human retina," IEEE Trans. Med. Imag., vol. 17, no. 4, pp. 606-619, Aug. 1998.
20. A. Hoover, V. Kouznetsova, and M. Goldbaum, "Locatingblood vessels in retinal images by piecewise threshold probing of a matched filter response," IEEE Trans. Med. Imag., vol. 19, no. 3, pp. 203-210, Mar. 2000.
21. X. Jiang and D. Mojon, "Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images," IEEE Pattern Anal. Mach. Intell., vol. 25, no. 1, pp. 131-137, Jan. 2003.
22. L. Zhou, M. S. Rzeszotarski, L. J. Singerman, and J. M. Chokreff, "The detection and quantification of retinopathy using digital angiograms," IEEE Trans. Med. Imag., vol. 13, no. 4, pp. 619-626, Dec. 1994.
23. R. Poli and G. Valli, "An algorithm for real-time vessel enhancement and detection," Comput. Meth. Programs Biomed., vol. 52, pp. 1-22, 1997.
24. L. Gagnon, M. Lalonde, M. Beaulieu, and M. C. Boucher, "Procedure to detect anatomical structures in optical fundus images," Proc SPIE (Med. Imag. Image Proces.), vol. 4322, pp. 1218-1225, 2001.
25. A. Can, H. Shen, J. N. Turner, H. L. Tanenbaum, and B. Roysam, "Rapid automated tracing and feature extraction from live high-resolution retinal fundus images using direct exploratory algorithms," IEEE Trans. Inf. Technol. Biomed, vol. 3, no. 2, pp. 125-138, Jun. 1999.
26. K. Fritzsche, A. Can, H. Shen, C. Tsai, J. Turner, H. L. Tanenbuam, C. V. Stewart, and B. Roysam, "Automated model based segmentation, tracing and analysis of retinal vasculature from digital fundus images," in Angiography and Plaque Imaging: Advanced Segmentation Techniques, J. S. Suri and S. Laxminarayan, Eds. Boca Raton, FL: CRC, 2002.
27. S. R. Aylward and E. Bullitt, "Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction," IEEE Trans. Med. Imag., vol. 21, no. 2, pp. 61-75, Feb. 2002.
28. F. Zana and J. C. Klein, "Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation," IEEE Trans. Image Process., vol. 10, no. 7, pp. 1010-1019, Jul. 2001.
29. T. Walter, J. C. Klein, P. Massin, and A. Erginay, "A contribution of image processing to the diagnosis of diabetic retinopathy detection of exudates in color fundus images of the human retina," IEEE Trans. Med. Imag., vol. 21, no. 10, pp. 1236-1244, Oct. 2002.
30. M. Fontaine, L. Macaire, J. G. Postaire, M. Valette, and P. Labalette, "Fundus images segmentation by unsupervised classification," presented at the Proc. VI'99 12th Conf. Vision InterfaceVision Interface, Trois-Rivieres, Canada, May 1999.
31. J. Staal, M. D. Abramoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, "Ridge-based vessel segmentation in color images of the retina," IEEE Trans. Med. Imag., vol. 23, no. 4, pp. 501-509, Apr. 2004.
32. V. Mahadevan, H. Narasimha-Iyer, B. Roysam, and H. L. Tanenbaum, "Robust model-based vasculature detection in noisy biomedical images," IEEE-Trans. Inf. Technol. Biomed., vol. 8, no. 3, pp. 360-376, 7 Sep. 2004.
33. K. Fritzsche, "Computer vision algorithms for retinal vessel width change detection and quantification," Ph.D. thesis, , Rensselaer Polytechnic Institute, Troy, NY, 2003.
34. G. Lin, K. L. Fritzsche, C. V. Stewart, H. L. Tanenbaum, and B. Roysam, "Predictive scheduling algorithms for real-time feature extraction and spatial referencing: application to retinal image sequences," IEEE-Trans. Biomed. Eng., vol. 51, no. 1, pp. 115-125, Jan. 2004.
35. H. Shen, B. Roysam, C. V. Stewart, J. N. Turner, and H. L. Tanenbaum, "Optimal scheduling of tracing computations for real-time vascular landmark extraction from retinal fundus images," IEEE-Trans. Inf. Technol. Biomed., vol. 5, no. 1, pp. 77-91, Mar. 2001.
36. M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: active contour models," Int. J. Comput. Vision, vol. 4, no. 321-331, 1988.
37. C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H. Williamson, "Automated localization of the optic disc, fovea, and retinal blood vessels from digital color fundus images," Br. J. Ophthalmol., vol. 83, no. 8, pp. 902-910, Aug. 1999.
38. A. Hoover and M. Goldbaum, "Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels," IEEE Trans. Med. Imag., vol. 22, no. 8, pp. 951-958, Aug. 2003.
39. G. K. Matsopoulos, N. A. Mouravliansky, K. K. Delibasis, and K. S. Nikita, "Automatic retinal image registration scheme using global optimization techniques," IEEE-Trans. Inf. Technol. Biomed., vol. 3, no. 1, pp. 47-60, Mar. 1999.
40. N. Ritter, R. Owens, J. Cooper, R. Eikelboom, and P. P. van Saarloos, "Registration of stereo and temporal images of the retina," IEEE Trans. Med. Imag., vol. 18, no. 5. pp. 404-418, May 1999.
41. C. V. Stewart, C.-L. Tsai, and B. Roysam, "The dual-bootstrap iterative closest point algorithm with application to retinal image registration," IEEE Trans. Med. Imag., vol. 22, no. 11, pp. 1379-1394, Nov. 2003.
42. A. Can, C. V. Stewart, B. Roysam, and H. L. Tanenbaum, "A feature-based robust hierarchical algorithm for registration pairs of images of the curved human retina," IEEE Trans Pattern Anal. Mach. Intell., vol. 24, no. 3, Mar. 2002.
43. R. C. Gonzalez and R. E. Woods, Digital Image Processing. Reading, MA: Addison-Wesley, 1992.
44. G. Finlayson and S. Hordley, "Improving gamut mapping color constancy," IEEE Tran. Image Process., vol. 9, no. 10, pp. 1774-1783, Oct. 2000.
45. D. J. Jobson, Z. Rahman, and G. A. Woodell, "Properties and performance of a center/surround retinex," IEEE Trans. Image Process., vol. 6, no. 3, pp. 451-462, Mar. 1997.
46. M. J. Carlotto, "Nonlinear background estimation and change detection for wide area search," Opt. Eng., vol. 39, no. 5, pp. 1223-1229, May 2000.
47. B. H. Brinkmann, A. Manduca, and R. A. Robb, "Optimized homomorphic unsharp masking for MR grayscale imhomogenity correction," IEEE Trans. Med. Imag., vol. 17, no. 2, pp. 161-171, Apr. 1998.
48. R. Guillemaud, "Uniformity correction with homomorphic filtering on region of interest," in Proc. IEEE Int. Conf. Image Processing, 1998, pp. 872-875.
49. D. Toth, T. Aach, and V. Metzler, "Illumination-invariant change detection," in Proc. 4th IEEE Southwest Symp. Image Analysis and Interpretation, 2000.
50. J. Lou, H. Yang, W. Hu, and T. Tan, "An illumination invariant change detection algorithm," presented at the 5th Asian Conf. Computer Vision, Melbourne, Australia, 2002.
51. R. Jain and K. Skifstad, "Illumination independent change detection from real world image sequences," Comput. Vis. Graph. Image Process., vol. 46, pp. 387-399, 1989.
52. Y. A. Plotnikov, N. Rajic, and W. P. Winfree. "Means of eliminating background effects for defect detection and visualization in infrared thermography," Opt. Eng., vol. 39, no. 4, 2000.
53. R. Highnam and M. Brady, "Model-based image enhancement of far infrared images," IEEE Trans Pattern Anal. Mach. Intell., vol. 19, no. 4, pp. 410-415, Apr. 1997.
54. M. F. Tappen, W. T. Freeman, and E. H. Adelson, "Recovering intrinsic images from a single image," in Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, 2003.
55. Y. Weiss, "Deriving intrinsic images from image sequences," presented at the 8th IEEE Int Conf. Computer Vision, Vancouver, BC, Canada, 2001.
56. G. D. Finlayson, S. D. Hordley, and M. S. Drew, "Removing shadows from images," in Proc. 7th European Conf. on Computer Vision, Copenhagen, Denmark, May 2002, vol. 4, pp. 823-836.
57. P. Rosin and E. Ioannidis, "Evaluation of global image thresholding for change detection," Pattern Recogni. Lett., vol. 24, no. 14, pp. 2345-2356, Oct. 2003.
58. P. Smits and A. Annoni, "Toward specification-driven change detection," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 3, pp. 1484-1488, May 2000.
59. J. P. Howarth and G. M. Wickware, "Procedure for change detection using Landsat digital data," Int. J. Remote Sens., vol. 2, pp. 277-291, 1981.
60. T. Aach and A. Kaup, "Bayesian algorithms for adaptive change detection in image sequences using Markov random fields," Signal Process.: Image Commun., vol. 7, pp. 147-160, Aug. 1995.
61. D. Toth, T. Aach, and V. Metzler, "Illumination-invariant change detection," in Proc. 4th IEEE Southwest Symp. Image Analysis and Interpretation, Austin, TX, 2000, pp. 3-7.
62. L. Bruzzone and D. F. Prieto, "Automatic analysis of the difference image for unsupervised change detection," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 3, pp. 1171-1182, May 2000.
63. M. J. Black, D. J. Fleet, and Y. Yacoob, "Robustly estimating changes in image appearance," Comput. Vis. Image Understanding, vol. 78, no. 1, pp. 8-31, 2000.
64. K. Skifstad and R. Jain, "Illumination independent change detection for real world image sequences," Comput. Vis., Graphics, Image Process., vol. 46, no. 3, pp. 387-399, 1989.
65. R. J. Radke, S. Andra, O. Al-Kofahi, and B. Roysam, "Image change detection algorithms: a systematic survey," IEEE Trans. Image Process., vol. 14, no. 3, Mar. 2005.
66. T. Kasetkasem and P. Varshney, "An image change detection algorithm based on Markov random field models," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 8, pp. 1815-1823, Aug. 2002.
67. J. Besag, "On the statistical analysis of dirty pictures," J. Roy. Statist. Soc. B, vol. 48, pp. 259-302, 1986.
68. R. Maronna and R. Zamar, "Robust estimates of location and dispersion for high dimensional data sets," Technometrics, 2002.