An approach to localize the retinal blood vessels using bit planes and centerline detection

Computer Methods and Programs in Biomedicine

Volumn 108, Issue 2, 2012, Pages 600-616

  Fraz, M M ^a   Barman, S A ^a   Remagnino, P ^a   Hoppe, A ^a   Basit, A ^b   Uyyanonvara, B ^c   Rudnicka, A R ^d   Owen, C G ^d  

^a KINGSTON UNIVERSITY   (United Kingdom)

^b PAKISTAN INSTITUTE OF NUCLEAR SCIENCE AND TECHNOLOGY   (Pakistan)

^c THAMMASAT UNIVERSITY   (Thailand)

^d ST GEORGE'S UNIVERSITY OF LONDON   (United Kingdom)

Author keywords

Bit plane slicing; Blood vessel segmentation; First order derivative of Gaussian; Image segmentation; Mathematical morphology; Medical imaging; Ocular fundus; Retinal image

Indexed keywords

BIT-PLANE SLICING; BLOOD VESSEL SEGMENTATION; GAUSSIANS; OCULAR FUNDUS; RETINAL IMAGE;

FORESTRY; IMAGE SEGMENTATION; MATHEMATICAL MORPHOLOGY; MEDICAL IMAGING; OPHTHALMOLOGY;

BLOOD VESSELS;

ACCURACY; ALGORITHM; ARTICLE; AUTOMATION; COLOR; DATA BASE; EVALUATION; EYE EXAMINATION; HUMAN; IMAGE ANALYSIS; IMAGE PROCESSING; IMAGE QUALITY; IMAGE RECONSTRUCTION; INTERMETHOD COMPARISON; MATHEMATICAL ANALYSIS; METHODOLOGY; MORPHOLOGY; QUANTITATIVE ANALYSIS; RETINA; RETINA BLOOD VESSEL; SENSITIVITY AND SPECIFICITY; TISSUE DISTRIBUTION;

HUMANS; RETINAL VESSELS;

ALGORITHMS; ANATOMY; BLOOD; IMAGE ANALYSIS; MAGNETIC RESONANCE; VESSELS;

EID: 84867404263     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2011.08.009     Document Type: Article

References (83)

1. Leung H., Wang J.J., Rochtchina E., Tan A.G., Wong T.Y., Klein R., Hubbard L.D., Mitchell P. Relationships between age, blood pressure, and retinal vessel diameters in an older population. Investigative Ophthalmology and Visual Science 2003, 44:2900-2904.
2. T.Y. Wong, R. Klein, A.R. Sharrett, B.B. Duncan, D.J. Couper, J.M. Tielsch, B.E.K. Klein, L.D. Hubbard, Retinal arteriolar narrowing and risk of coronary heart disease in men and women, JAMA: The Journal of the American Medical Association 287 (2002) 1153-1159. http://jama.ama-assn.org/content/287/9/1153.abstract.
3. Teng T., Lefley M., Claremont D. Progress towards automated diabetic ocular screening: A review of image analysis and intelligent systems for diabetic retinopathy. Medical and Biological Engineering and Computing 2002, 40:2-13.
4. Heneghan C., Flynn J., O'Keefe M., Cahill M. Characterization of changes in blood vessel width and tortuosity in retinopathy of prematurity using image analysis. Medical Image Analysis 2002, 6:407-429.
5. Haddouche A., Adel M., Rasigni M., Conrath J., Bourennane S. Detection of the foveal avascular zone on retinal angiograms using Markov random fields. Digital Signal Processing 2010, 20:149-154.
6. E. Grisan, A. Ruggeri, A divide et impera strategy for automatic classification of retinal vessels into arteries and veins. Engineering in Medicine and Biology Society, 2003. Proceedings of the 25th Annual International Conference of the IEEE, 2003.
7. M. Foracchia, Extraction and quantitative description of vessel features in hypertensive retinopathy fundus images, in: E. Grisan (Ed.), Book Abstracts 2nd International Workshop on Computer Assisted Fundus Image Analysis, 2001, p. 6.
8. Lowell J., Hunter A., Steel D., Basu A., Ryder R., Kennedy R.L. Measurement of retinal vessel widths from fundus images based on 2-D modeling. IEEE Transactions on Medical Imaging 2004, 23:1196-1204.
9. Kanski J.J. Clinical Ophthalmology 2007, Elsevier Health Sciences, London, UK. 6th ed.
10. Zana F., Klein J.C. A multimodal registration algorithm of eye fundus images using vessels detection and Hough transform. IEEE Transactions on Medical Imaging 1999, 18:419-428.
11. K. Fritzsche, A. Can, H. Shen, C. Tsai, J. Turner, H.L. Tanenbuam, C.V. Stewart, B. Roysam, J.S. Suri, S. Laxminarayan, Automated model based segmentation, tracing and analysis of retinal vasculature from digital fundus images, in: J.S. Suri, S. Laxminarayan (Eds.), State-of-The-Art Angiography, Applications and Plaque Imaging Using MR, CT, Ultrasound and X-rays, Academic Press, 2003, pp. 225-298.
12. Huiqi L., Chutatape O. Automated feature extraction in color retinal images by a model based approach. IEEE Transactions on Biomedical Engineering 2004, 51:246-254.
13. Mariño C., Penedo G., Penas M., Carreira J., Gonzalez F. Personal authentication using digital retinal images. Pattern Analysis and Application 2006, 9:21-33.
14. Staal J., Abramoff M.D., Niemeijer M., Viergever M.A., van Ginneken B. Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging 2004, 23:501-509.
15. Mendonca A.M., Campilho A. Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Transactions on Medical Imaging 2006, 25:1200-1213.
16. Sun K., Chen Z., Jiang S., Wang Y. Morphological multiscale enhancement, fuzzy filter and watershed for vascular tree extraction in angiogram. Journal of Medical Systems 2010, http://www.springerlink.com/content/e82n0944n6223004/, 10.1007/s10916-010-9466-3.
17. Sofka M., Stewart C.V. Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures. IEEE Transactions on Medical Imaging 2006, 25:1531-1546.
18. Lam B.S.Y., Hong Y. A novel vessel segmentation algorithm for pathological retina images based on the divergence of vector fields. IEEE Transactions on Medical Imaging 2008, 27:237-246.
19. Espona L., Carreira M.J., Ortega M., Penedo M.G. A Snake for Retinal Vessel Segmentation. Proceedings of the 3rd Iberian conference on Pattern Recognition and Image Analysis, Part II 2007, Springer-Verlag, Girona, Spain.
20. Chaudhuri S., Chatterjee S., Katz N., Nelson M., Goldbaum M. Detection of blood vessels in retinal images using two-dimensional matched filters. IEEE Transactions on Medical Imaging 1989, 8:263-269.
21. DRIVE, Digital Retinal Images for Vessel Extraction. 2004. http://www.isi.uu.nl/Research/Databases/DRIVE.
22. STARE, STructured Analysis of the Retina. 2000. http://www.ces.clemson.edu/~ahoover/stare.
23. MESSIDOR, Methods for Evaluating Segmentation and Indexing techniques Dedicated to Retinal Ophthalmology. 2004. http://messidor.crihan.fr/index-en.php.
24. Akita K., Kuga H. A computer method of understanding ocular fundus images. Pattern Recognition 1982, 15:431-443.
25. Nekovei R., Ying S. Back-propagation network and its configuration for blood vessel detection in angiograms. IEEE Transactions on Neural Networks 1995, 6:64-72.
26. Sinthanayothin C., Boyce J.F., Cook H.L., Williamson T.H. Automated localisation of the optic disc, fovea, and retinal blood vessels from digital colour fundus images. British Journal of Ophthalmology 1999, 83:902-910.
27. M. Niemeijer, J.J. Staal, B. van Ginneken, M. Loog, M.D. Abramoff, Comparative study of retinal vessel segmentation methods on a new publicly available database. SPIE Medical Imaging 5374 (2004) 648-656.
28. Soares J.V.B., Leandro J.J.G., Cesar R.M., Jelinek H.F., Cree M.J. Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification. IEEE Transactions on Medical Imaging 2006, 25:1214-1222.
29. Ricci E., Perfetti R. Retinal blood vessel segmentation using line operators and support vector classification. IEEE Transactions on Medical Imaging 2007, 26:1357-1365.
30. Osareh A., Shadgar B. Automatic blood vessel segmentation in color images of retina. Iranian Journal of Science and Technology Transaction B-Engineering 2009, 33:191-206.
31. Lupascu C.A., Tegolo D., Trucco E. FABC: Retinal vessel segmentation using AdaBoost. IEEE Transactions on Information Technology in Biomedicine 2010, 14:1267-1274.
32. Tolias Y.A., Panas S.M. A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering. IEEE Transactions on Medical Imaging 1998, 17:263-273.
33. Salem S., Salem N., Nandi A. Segmentation of retinal blood vessels using a novel clustering algorithm (RACAL) with a partial supervision strategy. Medical and Biological Engineering and Computing 2007, 45:261-273.
34. Ng J., Clay S.T., Barman S.A., Fielder A.R., Moseley M.J., Parker K.H., Paterson C. Maximum likelihood estimation of vessel parameters from scale space analysis. Image and Vision Computing 2010, 28:55-63.
35. Kande G.B., Subbaiah P.V., Savithri T.S. Unsupervised fuzzy based vessel segmentation in pathological digital fundus images. Journal of Medical Systems 2009, 34:849-858.
36. Villalobos-Castaldi F., Felipe-Riverón E., Sánchez-Fernández L. A fast, efficient and automated method to extract vessels from fundus images. Journal of Visualization 2010, 13:263-270.
37. Hoover A.D., Kouznetsova V., Goldbaum M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging 2000, 19:203-210.
38. Xiaoyi J., Mojon D. Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images. IEEE Transactions on Pattern Analysis and Machine Intelligence 2003, 25:131-137.
39. Yao C., Chen H.-j. Automated retinal blood vessels segmentation based on simplified PCNN and fast 2D-Otsu algorithm. Journal of Central South University of Technology 2009, 16:640-646.
40. Freeman W.T., Adelson E.H. The Design and Use of Steerable Filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 1991, 13:891-906.
41. Kochner B., Schuhmann D., Michaelis M., Mann G., Englmeier K.-H. Course tracking and contour extraction of retinal vessels from color fundus photographs: Most efficient use of steerable filters for model-based image analysis 1998, Image Processing, San Diego, CA, USA.
42. Gang L., Chutatape O., Krishnan S.M. Detection and measurement of retinal vessels in fundus images using amplitude modified second-order Gaussian filter. IEEE Transactions on Biomedical Engineering 2002, 49:168-172.
43. Zhang B., Zhang L., Zhang L., Karray F. Retinal vessel extraction by matched filter with first-order derivative of Gaussian. Computers in Biology and Medicine 2010, 40:438-445.
44. Al-Rawi M., Qutaishat M., Arrar M. An improved matched filter for blood vessel detection of digital retinal images. Computers in Biology and Medicine 2007, 37:262-267.
45. Cinsdikici M.G., Aydin D. Detection of blood vessels in ophthalmoscope images using MF/ant (matched filter/ant colony) algorithm. Computer Methods and Programs in Biomedicine 2009, 96:85-95.
46. Amin M., Yan H. High speed detection of retinal blood vessels in fundus image using phase congruency. Soft Computing - A Fusion of Foundations, Methodologies and Applications 2011, 15(6):1-14.
47. Zana F., Klein J.C. Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation. IEEE Transactions on Image Processing 2001, 10:1010-1019.
48. Ayala G., Leon T., Zapater V. Different averages of a fuzzy set with an application to vessel segmentation. IEEE Transactions on Fuzzy Systems 2005, 13:384-393.
49. Liu I., Sun Y. Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme. IEEE Transactions on Medical Imaging 1993, 12:334-341.
50. Liang Z., Rzeszotarski M.S., Singerman L.J., Chokreff J.M. The detection and quantification of retinopathy using digital angiograms. IEEE Transactions on Medical Imaging 1994, 13:619-626.
51. O. Chutatape, Z. Liu, S.M. Krishnan, Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters. Engineering in Medicine and Biology Society, 1998. Proceedings of the 20th Annual International Conference of the IEEE, 1998.
52. Quek F.K.H., Kirbas C. Vessel extraction in medical images by wave-propagation and traceback. IEEE Transactions on Medical Imaging 2001, 20:117-131.
53. Ali C., Hong S., Turner J.N., Tanenbaum H.L., Roysam B. Rapid automated tracing and feature extraction from retinal fundus images using direct exploratory algorithms. IEEE Transactions on Information Technology in Biomedicine 1999, 3:125-138.
54. Kelvin P., Ghassan H., Rafeef A. Live-vessel: extending livewire for simultaneous extraction of optimal medial and boundary paths in vascular images. Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention 2007, Springer-Verlag, Brisbane, Australia.
55. Frangi A.F., Niessen W.J., Vincken K.L., Viergever M.A., William W., Alan C., Scott D. Live-vessel: extending livewire for simultaneous extraction of optimal medial and boundary paths in vascular images. Lecture Notes in Computer Science. Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention MICCAI™98, vol. 1496 1998, 130. Springer, Berlin, Heidelberg.
56. Barrett W.A., Mortensen E.N. Interactive live-wire boundary extraction. Medical Image Analysis 1997, 1:331-341.
57. Delibasis K.K., Kechriniotis A.I., Tsonos C., Assimakis N. Automatic model-based tracing algorithm for vessel segmentation and diameter estimation. Computer Methods and Programs in Biomedicine 2010, 100:108-122.
58. M.E. Martinez-Perez, A.D. Hughes, A.V. Stanton, S.A. Thom, A.A. Bharath, K.H. Parker, Retinal blood vessel segmentation by means of scale-space analysis and region growing. Proceedings of the Second International Conference on Medical Image Computing and Computer-Assisted Intervention, London, UK, 1999.
59. Martinez-Perez M.E., Hughes A.D., Thom S.A., Bharath A.A., Parker K.H. Segmentation of blood vessels from red-free and fluorescein retinal images. Medical Image Analysis 2007, 11:47-61.
60. L. Ibanez, W. Schroeder, L. Ng, J. Cates, The ITK Software Guide.: Kitware, Inc. ISBN 1-930934-10-6, 2003. http://www.itk.org/ItkSoftwareGuide.pdf.
61. Palomera-Perez M.A., Martinez-Perez M.E., Benitez-Perez H., Ortega-Arjona J.L. Parallel multiscale feature extraction and region growing: Application in retinal blood vessel detection. IEEE Transactions on Information Technology in Biomedicine 2010, 14:500-506.
62. Wink O., Niessen W.J., Viergever M.A. Multiscale vessel tracking. IEEE Transactions on Medical Imaging 2004, 23:130-133.
63. Vlachos M., Dermatas E. Multi-scale retinal vessel segmentation using line tracking. Computerized Medical Imaging and Graphics 2009, 34:213-227.
64. Vermeer K.A., Vos F.M., Lemij H.G., Vossepoel A.M. A model based method for retinal blood vessel detection. Computers in biology and medicine 2004, 34:209-219.
65. Mahadevan V., Narasimha-Iyer H., Roysam B., Tanenbaum H.L. Robust model-based vasculature detection in noisy biomedical images. IEEE Transactions on Information Technology in Biomedicine 2004, 8:360-376.
66. Huber P.J. A robust version of the probability ratio test. The Annals of Mathematical Statistics 1965, 36:1753-1758.
67. Field C., Smith B. Robust estimation - A weighted maximum-likelihood approach. International Statistical Review 1994, 62:405-424.
68. Ronchetti E. Robust model selection in regression. Statistics and Probability Letters 1985, 3:21-23.
69. Li W., Bhalerao A., Wilson R. Analysis of retinal vasculature using a multiresolution hermite model. IEEE Transactions on Medical Imaging 2007, 26:137-152.
70. Lam B.S.Y., Yongsheng G., Liew A.W.C. General retinal vessel segmentation using regularization-based multiconcavity modeling. IEEE Transactions on Medical Imaging 2010, 29:1369-1381.
71. Narasimha-Iyer H., Beach J.M., Khoobehi B., Roysam B. Automatic identification of retinal arteries and veins from dual-wavelength images using structural and functional features. IEEE Transactions on Biomedical Engineering 2007, 54:1427-1435.
72. G. Xiaohong, A. Bharath, A. Stanton, A. Hughes, N. Chapman, S. Thom, A method of vessel tracking for vessel diameter measurement on retinal images. Proceedings of International Conference on Image Processing, 2001.
73. Zhu T. Fourier cross-sectional profile for vessel detection on retinal images. Computerized Medical Imaging and Graphics 2010, 34:203-212.
74. Al-Diri B., Hunter A. A ribbon of twins for extracting vessel boundaries. The 3rd European Medical and Biological Engineering Conference 2005, Prague, Czech Republic.
75. Al-Diri B., Hunter A., Steel D. An active contour model for segmenting and measuring retinal vessels. IEEE Transactions on Medical Imaging 2009, 28:1488-1497.
76. Gooya A., Hongen L., Matsumiya K., Masamune K., Masutani Y., Dohi T. A Variational Method for Geometric Regularization of Vascular Segmentation in Medical Images. IEEE Transactions on Image Processing 2008, 17:1295-1312.
77. Lorigo L.M., Faugeras O.D., Grimson W.E.L., Keriven R., Kikinis R., Nabavi A., Westin C.F. CURVES: Curve evolution for vessel segmentation. Medical Image Analysis 2001, 5:195-206.
78. Sum K.W., Cheung P.Y.S. Vessel Extraction Under Non-Uniform Illumination: A Level Set Approach. IEEE Transactions on Biomedical Engineering 2008, 55:358-360.
79. Fraz M.M., Remagnino P., Hoppe A., Uyyanonvara B., Barman S.A. Retinal Vessel Extraction using First-order Derivative of Gaussian and Morphological Processing. 7th International Symposium on Visual Computing 2011.
80. López A.M., Lloret D., Serrat J., Villanueva J.J. Multilocal creaseness based on the level-set extrinsic curvature. Computer Vision and Image Understanding 2000, 77:111-144.
81. Serra J. Image Analysis and Mathematical Morphology 1983, Academic Press, Inc, Orlando, FL, USA.
82. Rangayyan R.M. Biomedical Image Analysis 2004, CRC Press, Boca Raton.
83. M.M. Fraz, M.Y. Javed, A. Basit, Retinal Vessels Extraction Using Bitplanes. Eight IASTED International Conference on Visualization, Imaging, and Image Processing, Palma De Mallorca, Spain, 2008.