The Detection and Quantification of Retinopathy Using Digital Angiograms

IEEE Transactions on Medical Imaging

Volumn 13, Issue 4, 1994, Pages 619-626

  Zhou, Liang ^a   Rzeszotarski, Mark S ^a   Chokreff, Jeanne M ^a  

^a CASE WESTERN RESERVE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ANGIOGRAPHY; BLOOD VESSELS; DIGITAL SIGNAL PROCESSING; ESTIMATION; FUNCTIONS; IMAGE PROCESSING; MATHEMATICAL MODELS; SIGNAL FILTERING AND PREDICTION;

ADAPTIVE DENSITOMETRIC TRACKING TECHNIQUE; AUTOMATED VESSEL TRACKING PROGRAM; DIGITAL ANGIOGRAMS; GAUSSIAN FUNCTIONS; GLOBAL THRESHOLD METHOD; MATCHED FILTERING APPROACH; RETINOPATHY;

MEDICAL IMAGING;

EID: 0028706756     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/42.363106     Document Type: Article

References (36)

1. American Academy of Ophthalmology, “Retina and vitreous,” in Basic and Clinical Science Course, section 11, pp. 13-27, 31-39, 1991.
2. V. Patel, et al., “A new concept in accurately measuring retinal vessel diameter from transmittance and densitometry profile of fundus photographs,” ARVO Abstract. Invest. Ophthalmol. Vis. Sci., vol. 33, no. 4, p. 804, 1992.
3. F. C. Delori, K. A. Fitch, G. T. Feke, D. M. Deupree, and J. J. Weiter, “Evaluation of micrometric and microdensitometric methods for measuring the width of retinal vessel images on fundus photographs,” Graefe’s Arch. Clin. Exp. Ophthalmol., vol. 226, pp. 393-399, 1986.
4. B. Khoobehi, G. A. Peyman, N. Bhatt, and D. Moshfeghi, “Laser-induced experimental vascular occlusion using liposome-encapsulated ADP,” Lasers in Surgery and Medicine, vol. 12, pp. 610-614, 1992.
5. P. Jensen, et al, “Video kymography of human retinal vessel diameter in vivo “ ARVO Abstract. Invest. Ophthalmol. Vis. Sci., vol. 33, no. 4, p. 1048, 1992.
6. B. Khoobehi, et al, “Relationship between blood velocity and retinal vessel diameter,” ARVO Abstract. Invest. Ophthalmol. Vis. Sci., vol. 33, no. 4, p. 810, 1992.
7. H. Chen, et al., “Retinal vessel diameter changes in the cardiac cycle,” ARVO Abstract. Invest. Ophthalmol. Vis. Sci., vol. 33, no. 4, p. 811, 1992.
8. J. Wiek, et al., “Blood flow measurements and vessel diameter changes in patients with retinal vein occlusion: A preliminary study,” ARVO Abstract. Invest Ophthalmol. Vis. Sci., vol. 33, no. 4, p. 1083, 1992.
9. P. Kador, et al., “Retinal changes in galactose-fed dogs: Intervention studies,” ARVO Abstract. Invest. Ophthalmol. Vis. Sci., vol. 33, no. 4, p. 878, 1992.
10. T. Takamoto, et al., “The effect of timolol on retinal vessel width of ocular hypertensive eyes,” ARVO Abstract. Invest. Ophthalmol. Vis. Sci., vol. 33, no. 4, p. 1078, 1992.
11. N. Fujjio, et al., “Retinal circulatory abnormalities in branch retinal vessel occlusion,” ARVO Abstract. Invest. Ophthalmol. Vis. Sci., vol. 33, no. 4, p. 1084, 1992.
12. E. Peli, et al., “Image enhancement for the visually impaired,” Invest. Ophthalmol. Vis. Sci., vol. 32, no. 8, pp. 2337-2350, 1991.
13. R. C. Gonzalez and R. E. Woods, Digital Image Processing. Reading, MA: Addison-Wesley, 1992, pp. 161-218, 429-455.
14. W. K. Pratt, Digital Image Processing. New York: Wiley, 1978.
15. H. L. Tan, et al., “A cost minimization approach to edge detection using simulated annealing,” IEEE Trans. Pattern Anal. Machine Intell., vol. 14, no. 1, pp. 3-18, Jan. 1992.
16. C. Chen, et al., “Edge and surface searching in medical images,” in Medical Imaging II, SPIE, vol. 914, pp. 594-599, 1992.
17. S. Chaudhuri, et al., “Detection of blood vessel in retinal image using two-dimensional mateched filters,” IEEE Trans. Med. Imag., vol. 8, no. 3, Sept. 1989.
18. J. S. J. Lee, et al., “Morphological edge detection,” IEEE Trans. Robotics Automat., vol. RA-3, no. 2, pp. 142-156, Apr. 1987.
19. D. L. Pope, et al., “Dynamic search algorithms in left ventricular border recognition and analysis of coronary arteries,” Proc. IEEE Comput. Cardiol., pp. 71-75, 1984.
20. G. Besson, “Vascular segment using ' ‘snake’ transforms and region growing,” in Medical Imaging III: in Image Processing, SPIE, vol. 1092, pp. 429-435, 1988.
21. K. R. Hoffmann, et al., “Three-Dimension reproduction of coronary vascular trees using the double-square-box method of tracking,” in Medical Imaging II, SPIE, vol. 914, pp. 375-378, 1988.
22. D. Ergener, et al., “Sequential analysis of angiograms by an expert system,” in Medical Image II, SPIE, vol. 914, pp. 613-619, 1988.
23. Y. Sun, “Knowledge-based segmentation and correspondence of vascular structures from biplane angiograms,” Medical Imaging TV: Image Processing, SPIE, vol. 1233, pp. 257-265, 1990.
24. K. Barth, et al., “Automated biplane vessel recognition in digital coronary angiograms,” in Medical Imaging IV: Imaging Processing, SPIE, vol. 1233, pp. 266-275, 1990.
25. K. Barth, et al., “A critical examination of angiographicstenosis quantitation by digital image processing,” Proc. IEEE Med. Imag. Image Interpretation, pp. 71-76, 1982.
26. T. N. Pappas and J. S. Lim, “Estimation of coronary artery boundaries in angiograms,” in Application of Digital Image Processing VII, SPIE, vol. 504, 312-321, 1984.
27. T. Sandor, et al., “High precision quantitative angiography,” IEEE Trans. Med. Imag., vol. MI-6, no. 3, pp. 258-265, Sept. 1987.
28. A. V. Oppenheim and R. W. Schafer, Digital Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1975.
29. P. H. Eichel, et al., “A method for fully automatic definition of coronary arterial edges from cineangiograms,” Proc. IEEE Comput. Cardiol., pp. 201-204, 1987.
30. S. R. Fleagle, et al., “Geometric validation of a robust method of automated edge detection in clinical coronary arteriography,” Proc. IEEE Comput. Cardiol., pp. 197-200, 1987.
31. J. W. Modestino and R. W. Fries, “Edge detection in noisy images using recursive digital filtering,” Comput. Graphics Image Processing, vol. 6, pp. 409-433, 1977.
32. L. J. Starr, “Digital angiography of the ocular fundus without fluorescein,” Medical Imaging II, SPIE, vol. 914, pp. 496-499, 1988.
33. W. Barrett, et al., “Automated detection of coronary arteries and quantitation of percent stenosis from DSA image,” in Proc. IEEE Comput. Cardiol., pp. 123-126, 1984.
34. P. Suetens, et al.,” Knowledge-based blood vessel delineation on DSA images,” in Medical Imaging II, SPIE, vol. 914, pp. 406-411, 1988.
35. N. Srinivasa, et al., “Detection of edges from projections,” IEEE Trans. Med. Imag., vol. 11, no. 1, pp. 76-80, Mar. 1992.
36. I. Biederman, “Human visual pattern recognition of medical images,” in Medical Imaging IV: Imaging Processing, SPIE, vol. 1233, pp. 2-7, 1990.