Automated diagnosis of retinopathy by content-based image retrieval

Retina

Volumn 28, Issue 10, 2008, Pages 1463-1477

  Chaum, Edward ^a,d   Karnowski, Thomas P ^b   Govindasamy, V Priya ^b   Abdelrahman, Mohamed ^c   Tobin, Kenneth W ^b  

^a UNIVERSITY OF TENNESSEE HEALTH SCIENCE CENTER   (United States)

^b OAK RIDGE NATIONAL LABORATORY   (United States)

^c Technological University   (United States)

^d Hamilton Eye Institute   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; COMPUTER ASSISTED DIAGNOSIS; DATA BASE; DIABETIC RETINOPATHY; DIAGNOSTIC ACCURACY; HUMAN; IMAGE ANALYSIS; IMAGE RETRIEVAL; INFORMATION PROCESSING; LIBRARY; PREDICTION; PROLIFERATIVE RETINOPATHY; RETINA IMAGE; RETINA MACULA AGE RELATED DEGENERATION; RETINOPATHY; SEMANTICS; SENSITIVITY AND SPECIFICITY; STORAGE; ARTIFICIAL INTELLIGENCE; BIOLOGY; INFORMATION RETRIEVAL; METHODOLOGY; PATHOLOGY; PHOTOGRAPHY; RETINA BLOOD VESSEL; RETINA MACULA DEGENERATION;

ARTIFICIAL INTELLIGENCE; COMPUTATIONAL BIOLOGY; DIABETIC RETINOPATHY; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; INFORMATION STORAGE AND RETRIEVAL; MACULAR DEGENERATION; PHOTOGRAPHY; RETINAL VESSELS; SENSITIVITY AND SPECIFICITY;

EID: 58949091123     PISSN: 0275004X     EISSN: None     Source Type: Journal    
DOI: 10.1097/IAE.0b013e31818356dd     Document Type: Article

References (59)

1. The Diabetic Retinopathy Study Research Group. Preliminary report on effects of photocoagulation therapy. Am J Ophthalmol 1976;81:383-396.
2. The Early Treatment Diabetic Retinopathy Study Research Group. Photocoagulation for diabetic macular edema: early treatment diabetic retinopathy study report number 1. Arch Ophthalmol 1985;103:1796-1806.
3. Age-Related Eye Disease Study Research Group. Risk factors associated with age-related macular degeneration. A case-control study in the age-related eye disease study: age-related eye disease study report number 3. Ophthalmol 2000;107: 2224-2232.
4. Larsen M, Godt J, Grunkin M. Automated detection of diabetic retinopathy in a fundus photographic screening population. Invest Ophthal Vis Sci 2003;44:767-771.
5. Teng T, Lefley M, Claremont D. Progress towards automated diabetic ocular screening: a review of image analysis and intelligent systems for diabetic retinopathy. Med Biol Eng Comput 2002;40:2-13.
6. Patton N, Aslam TM, MacGillivary T, et al. Retinal image analysis: concepts, applications and potential. Prog Retin Eye Res 2006;25:99-127.
7. Foracchia M, Grisan E, Ruggeri A. Luminosity and contrast normalization in retinal images. Med Image Anal 2005;9:179- 190.
8. Martinez-Perez ME, Hughes AD, Stanton AV, et al. Segmentation of retinal blood vessels based on the second directional derivative and region growing. In: IEEE International Conference on Image Processing. Kobe, Japan 1999:173-176.
9. Lin T, Zheng Y. Adaptive image enhancement for retinal blood vessel segmentation. Electronic Lett 2002;38:1090- 1091.
10. Staal J, Abramoff MD, Niemeijer M, et al. Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 2004;23:501-509.
11. Hoover A, Goldbaum M. Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans Med Imaging 2003;22:951-958.
12. Foracchia M, Grisan E, Ruggeri A. Detection of the optic disc in retinal images by means of a geometrical model of vessel structure. IEEE Trans Med Imaging 2004;23:1189-1195.
13. Fleming AD, Goatman KA, Philip S, et al. Automatic detection of retinal anatomy to assist diabetic retinopathy screening. Phys Med Biol 2007;52:331-345.
14. Abramoff MD, Niemeijer M. The automatic detection of the optic disc location in retinal images using optic disc location regression. Presented at: 28th IEEE EMBS Annual Conference. New York, NY 2006.
15. Abramoff MD, Alward WL, Greenlee EC, et al. Automated segmentation of the optic disc from stereo color photographs using physiologically plausible features. Invest Ophthalmol Vis Sci 2007;48:1665-1673.
16. Morris DT, Donnison C. Identifying the neuroretinal rim boundary using dynamic contours. Image Vis Comput 1999; 17:169-174.
17. Li H, Chutatape O. Automated feature extraction in color retinal images by a model based approach. IEEE Trans Biomed Eng 2004;51:246-254.
18. Niemeijer M, Abramoff MD, van Ginneken B. Segmentation of the optic disc, macula and vascular arch in fundus photographs. IEEE Trans Med Imaging 2007;26:116-127.
19. Pinz A, Bernogger S, Datlinger P, Kruger A. Mapping the human retina. IEEE Trans Med Imaging 1998;17:606-619.
20. Solouma NH, Youssef AM, Badr YA, Kadah YM. A new real-time retinal tracking system for image-guided laser treatment. IEEE Trans Biomed Eng 2002;49:1059-1067.
21. Trucco E, Kamat PJ. Locating the optic disk in retinal images via plausible detection and constraint satisfaction. In: IEEE International Conference on Image Processing. Singapore: 2004:55-158.
22. Niemeijer M, van Ginneken B, Staal J, et al. Automatic detection of red lesions in digital color fundus photographs. IEEE Trans Med Imaging 2005;24:584-592.
23. Rapantzikos K, Zervakis M, Balas K. Detection and segmentation of drusen deposits on human retina: potential in the diagnosis of age-related macular degeneration. Med Image Anal 2003;7:95-108.
24. Ben Sbeh Z, Cohen LD, Mimoun G, Coscas G. A new approach of geodesic reconstruction for drusen segmentation in eye fundus images. IEEE Trans Med Imaging 2001;20:1321- 1333.
25. Tobin KW, Karnowski TP, Arrowood LF, et al. Content-based image retrieval for semiconductor process characterization. EURASIP J Appl Signal Process 2002;7:704-713.
26. Karnowski TP, Tobin KW, Ferrell RK, Lakhani F. Using an image retrieval system for image data management. Design, process integration, and diagnostics in IC manufacturing. Proc SPIE 2002;4692:120.
27. Santini S. Exploratory Image Databases, Content-Based Retrieval. Duluth, MN: Academic Press; 2001.
28. Duda RO, Hart PE, Stork DG. Pattern Classification. 2nd ed. New York, NY: John Wiley & Sons, Inc; 2001.
29. Arya S, Mount DM, Netanyahu NS, et al. An Optimal algorithm for approximate nearest neighbor searching in fixed dimensions. Journal ACM 1998;45:891-923.
30. Gong Y, Chuan CH, Xiaoyi G. Image indexing and retrieval based on color histogram. Multimedia Tools Appl 1996;2:133.
31. Rubner Y, Tomasi C. Perceptual Metrics for Image Database Navigation. Boston, MA: Kluwer Academic Publishers; 2001.
32. Vicario E, ed. Image Description and Retrieval. New York, NY: Plenum Press; 1998.
33. Mehtre BM, Kankanhalli MS, Lee WF. Shape Measures for content based image retrieval: a comparison. Inform Process Manag 1997;33:319.
34. Gary JE, Mehrotra R. Similar shape retrieval using a structural feature index. Information Syst 1993;18:525-545.
35. Bradley A. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recog 1997;30:1145-1159.
36. Bingham PR, Price JR, Tobin KW, Karnowski TP. Semiconductor sidewall shape estimation. SPIE Journal of Electronic Imaging, 2004;13:474-485.
37. Manjunath BS, Ma WY. Texture features for browsing and retrieval of image data. IEEE Trans Pattern Anal Mach Intell 1996;18:837-842.
38. Tobin KW, Chaum E, Govindasamy VP, Karnowski TP, Sezer O. Characterization of the optic disk in retinal imagery using a probabilistic approach. In: SPIE International Symposium on Medical Imaging. San Diego, CA: Proceedings of SPIE; 2006.
39. Karnowski TP, Govindasamy VP, Tobin KW, Chaum E. Locating the optic nerve in retinal images: comparing model-based and Bayesian decision methods. Conf Proc IEEE Eng Med Biol Soc 2006;1:4436-4439.
40. Tobin KW, Chaum E, Govindasamy VP, Karnowski TP. Detection of anatomic structures in human retinal imagery. IEEE Trans Med Imaging 2007;26:1729-1739.
41. Zana F, Klein J. Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Trans Image Process 2001;10:1010-1019.
42. Tobin K, Bhaduri B, Bright E, et al., Automated feature generation in large-scale geospatial libraries for content-based indexing. Photogrammetric Eng Remote Sens 2006;72:531- 540.
43. Muller H, Michoux N, Bandon D, Geissbuhler A. A review of content-based image retrieval systems in medical applications - clinical benefits and future directions. Int J Med Inf 2004;73:1-23.
44. Stellingwerf C, Hardus P, Hooymans J. Two-field photography can identify patients with vision-threatening diabetic retinopathy: a screening approach in the primary care setting. Diabetes Care 2001;24:2086-2090.
45. Lin DY, Blumenkranz MS, Brothers RJ, Grosvenor DM. The sensitivity and specificity of single-field nonmydriatic monochromatic digital fundus photography with remote image interpretation for diabetic retinopathy screening: a comparison with ophthalmoscopy and standardized mydriatic color photography. Am J Ophthalmol 2002;134:204-213.
46. Lim JI, Labree L, Nichols T. Comparison of nonmydriatic digitized video fundus images with standard 35-mm slides to screen for specific lesions of age-related macular degeneration. Retina 2002;22:59-64.
47. Dichtl A, Jonas JB, Mardin CY. Comparison between tomographic scanning evaluation and photographic measurement of the neuroretinal rim. Am J Ophthalmol 1996;121:494-501.
48. Iester M, DeFerrari R, Zanini M. Topographic analysis to discriminate glaucomatous from normal optic nerve heads with a confocal scanning laser: new optic disk analysis without any observer input. Surv Ophthalmol 1999;44:33-40.
49. Qing ZH. Segmentation of blood vessels in retinal images using 2-D entropies of gray level-gradient co-occurrence matrix. In: IEEE International Conference on Acoustics, Speech, and Signal Processing. Montreal, Quebec 2004:509-512.
50. Morgan-Davies J, Taylor NK, Armbrecht AM, Aspinall P, Dhillon B. Progressive assessment of age related macular degeneration using an artificial neural network approach. Br J Ophthalmol 2001;85:246-247.
51. Estabridis K, Defigueiredo R. Fovea and vessel detection via multi-resolution parameter transform. In: Medical Imaging. San Diego, CA: Proceedings of SPIE; 2007.
52. Kondermann C, Kondermann D, Yan M. Blood vessel classification into arteries and veins in retinal images. In: Medical Imaging. San Diego, CA: Proceedings of SPIE; 2007;6512: 651247.
53. Wu C-H, Agam G. Probabilistic retinal vessel segmentation. In: Medical Imaging. San Diego, CA: Proceedings of SPIE; 2007.
54. Lee SC, Lee ET, Kingsley RM, et al. Comparison of diagnosis of early retinal lesions of diabetic retinopathy between a computer system and human experts. Arch Ophthalmol 2001;119: 509-515.
55. Sinthanayothin C, Boyce JF, Williamson TH, et al. Automated detection of diabetic retinopathy on digital fundus images. Diabet Med 2002;19:105-112.
56. Ke Huang K, Yan M, Aviyente S. Edge directed inference for microaneurysms detection in digital fundus images. In: Medical Imaging. San Diego, CA: Proceedings of SPIE; 2007.
57. Narasimha-Iyer H, Can A, Roysam B, et al. Robust detection and classification of longitudinal changes in color retinal fundus images for monitoring diabetic retinopathy. IEEE Trans Biomed Eng 2006;53:1084-1098.
58. Antani S, Long LR, Thoma GR. Content-based image retrieval for large biomedical image archives. Medinfo 2004;11:829-833.
59. Lehmann TM, Guld MO, Deselaers T, et al. Automatic categorization of medical images for content-based retrieval and data mining. Comput Med Imaging Graph 2005;29:143-155.