Automatic blood vessel segmentation in color images of retina

Iranian Journal of Science and Technology, Transaction B: Engineering

Volumn 33, Issue 2, 2009, Pages 191-206

  Osareh, A ^a   Shadgar, B ^a  

^a SHAHID CHAMRAN UNIVERSITY OF AHVAZ   (Iran)

Author keywords

Gabor filters; Retinal blood vessels; Support vector machines; Vessel segmentation

Indexed keywords

AUTOMATED IMAGE PROCESSING; AUTOMATED METHODS; BLOOD VESSEL SEGMENTATION; COLOR IMAGES; DIABETIC RETINOPATHY; EARLY DETECTION; FEATURE VECTORS; GABOR FILTER; GABOR FILTERS; GAUSSIAN MIXTURE MODEL; IMAGE PIXELS; RECEIVER OPERATING CHARACTERISTIC CURVES; RETINAL BLOOD VESSELS; SCREENING SYSTEM; VESSEL SEGMENTATION;

BLOOD; EYE PROTECTION; IMAGE ENHANCEMENT; IMAGE RETRIEVAL; OPHTHALMOLOGY; SUGAR (SUCROSE); SUPPORT VECTOR MACHINES; VECTORS;

BLOOD VESSELS;

AUTOMATION; CARDIOVASCULAR SYSTEM; COLOR; DIABETES; EYE; VISION;

EID: 67749133713     PISSN: 10286284     EISSN: None     Source Type: Journal    
DOI: None     Document Type: Article

References (36)

1. Klein, R., Klein, B., Moss, S., Davis, M. & Demets,. D. (1984). The Wisconsin-epidemiologic study of diabetic retinopathy II. Prevalence and risk of diabetic retinopathy when age at diagnosis is less than 30 years. Archives of Ophthalmology, Vol. 102, pp. 520-526.
2. Early treatment diabetic retinopathy study research group. (1991). Early photocoagulation for diabetic retinopathy: ETDRS Report 9. Ophthalmology, Vol. 98, pp. 766-785.
3. th National Congress of the Australian Institute of Physics, Canberra, Australia, pp. 17-25.
4. Chaundhuri, S-., Chatterjee, S., Katz, N., Nelson, M. & Goldbaum, M. (1989). Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Trans. on Medical Imaging, Vol. 8, No. 3, pp. 263-269.
5. Pinz, A., Bernogger, S., Datlinger, P. & Kruger, A. (1998). Mapping the human retina. IEEE Trnas. on Medical Imaging, Vol. 17, No. 4, pp. 606-619.
6. Nekovei, R. & Sun, Y. (1995). Back propagation network and its configuration for blood vessel detection in angiograms. IEEE Trans. on Neural Networks, Vol. 6, No. 1, pp. 64-72.
7. Tamura, S., Tanaka, K., Ohmori, S. & Hoshi, M. (1983). Semiautomatic leakage analyzing system for time series fluorescein ocular fundus angiopraghy. Pattern Recognition, Vol. 16, No. 2, pp. 149-162.
8. Cote, B., Hart, W., Goldbaum, M., Kube, P. & Nelson, M. (1994). Classification of blood vessels in ocular fundus imgaes, technical report. Computer Science and Engineering Dept, University of California.
9. th Brazilian Symposium on Computer Graphics and Image Processing, pp. 84-90.
10. Soares, J., Leandro, J., Cesar, R., Jelinek, H., & Cree, M. (2006). Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Trans. on Medical Imaging, Vol. 25, No. 9, pp. 1214-1222.
11. Niemeijer, M., Staal, J., Ginneken, B., Loog, M. & Abramoff, M. (2004). Comparative study of retinal vessel segmentation methods on a new publicly available database. SPIE Medical Imaging, Vol. 5370, pp. 648-565.
12. Tamura, S., Okamoto, Y. & Yanashima, K. (1988). Zero-crossing interval corrections in tracing eye-fundus blood vessels. Pattern Recognition, Vol. 21, No. 3, pp. 227-233.
13. Tolias, Y. & Panas, S. (1998). A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Trnas. on Medical Imaging, Vol. 17, 2, pp. 263-273.
14. Fischer, M., Tenenbaum, J. & Wolf, H. (1981). Detection of roads and linear structures in low resolution aerial imagery using a multisource knowledge integration technique. Computer Graphics and Image Processing, Vol. 15, No. 3, pp. 201-223.
15. Jasiobedzki, P., Mcleod, D. & Taylor, C. (1991). Detection of non-perfused zones in retinal images. Computer-Based Medical Systems: Fourth Annual IEEE Symposium, pp. 162-169.
16. Zang, F. & Kelin, J. (2001). Segmentation of vessel like patterns using mathematical morphology. IEEE Trans. on Medical Imaging, Vol. 10, No. 7, pp. 1010-1019.
17. Simo, A. & de Ves, E. (2001). Segmentation of macular fluorescen angiographies, a statistical approach. Pattern Recognition, Vol. 34, No. 4, pp. 795-809.
18. Al-Rawi, M. & Karajeh, H. (2007). Genetic algorithm matched filter optimization for automated detection of blood vessels from digital retinal images. Computer Methods and Programs in Biomedicine, Vol. 87, pp. 248-253.
19. Hoover, A., Kouznetsov, V. & Goldbaum, M. (2000). Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Trans. on Medical Imaging, Vol. 19, No. 3, pp. 203-210.
20. Zang, F. & Klein, J. (1999). A multimodal registration algorithm of eye fundus images using vessels detection and Hough transform. IEEE Trans. on Medical Imaging, Vol. 18, No. 5, pp. 419-428.
21. Jiang, X. & Mojon, D. (2003). Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 25, No. 1, pp. 131-137.
22. Staal, J., Abramoff, M., Niemeijer, M. & Viergever, M. (2004). Ridge-based vessel segmentation in color images of retina. IEEE Trans. on Medical Imaging, Vol. 23, No. 4, pp. 501-509.
23. Drimbarean, A. & Whelan, P. (2001). Experiments in color texture analysis. Pattern Recognition Letters, Vol. 22, No. 10, pp. 1161-1167.
24. Nestares, O., Navarro, R., Portilla, J. & Tabernero, A. (1998). Efficeint sapatial-domain implementation of a multiscale image representation based on Gabor functions. Journal of Electronic Imaging, Vol. 7, No. 1, pp. 166-173.
25. Schiele, B. & Crowley, J. (2000). Recognition without correspondence using multidimensional receptive field histograms. Int. Journal of Computer Vision, Vol. 36, No. 1, pp. 31-52.
26. Sangwine, S., Home, R. (1998). The color image processing handbook. Chapman & Hall.
27. Duda, R., Hart, P. & Stork, D. (2000). Pattern classification. John Wiley & Sons.
28. Alpaydin, E. (2004). Introduction to machine learning. MIT Press.
29. Vapnik, V. (1995). The nature of statistical learning theory. Springer-Verlag.
30. Burges, J. (1998). A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, Vol. 2, No. 2, pp. 121-167.
31. Rantanen, V., Denessiousk, K., Gyllenberg, M., Koski, T. & Johnson, M. (2001). A fragment library based on Gaussian mixtures predicting favourable molecular interactions, Journal of Molecular Biology, Vol. 313, pp. 197-214.
32. Bishop, C. (1995). Neural networks for pattern recognition. Oxford University Press.
33. Nabney, I. (2002). Netlab algorithms for pattern recognition. Springer-Verlag.
34. Rabbani, H., Vafadoost, M. (2006). Wavelet based image denoising based on a mixture of laplace distributions, Iranian Journal of Science and Technology, Transaction B, Engineering, Vol. 30, No. B6, pp. 711-733.
35. Rissanen, J. (1983). A universal prior for integers and estimation by minimum description length, Annals of Statistics, Vol. 11, pp. 416-431.
36. Metz, C. (1986). ROC methodology in radiological imaging. Investigate Radiology, Vol. 21, pp. 720-733.