Retinal Blood Vessel Segmentation using Phase Congruency Model
Abstract
Retinal blood vessels are the only part of blood
circulation that can be observed directly. Doctors manually segment retinal images to evaluate the progression of diseases like diabetes and hypertension. The manual tracking is a tedious process. The retinal images have low and uneven contrast with respect to the background. Thus, a robust model is needed for describing the intensity variations for segmenting the vessels. Phase congruency based edge detector using the principal moments is very well adapted to this description. The paper purposes a simple contrast invariant retinal image segmentation using phase congruency model with a good edge localization. Initial results using STARE database (average sensitivity of 0.94, specificity of 0.66 and average accuracy of 0.91) are promising and comparable with other techniques in literature.
Keywords
Full Text:
PDFReferences
KansKy, Clinical Ophthalmology, Butterworh-Heinmann , London,
S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum,
“Detection of blood vessels in retinal images using two-dimensional
matched filters” , IEEE Trans. Med. Imag., Sep. 1989, vol. 8, no. 3, pp.
–269.
Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in
retinal images by piece-wise threshold probing of a matched filter
response”, IEEE Trans. Med. Imag., Mar. 2000, vol. 19, no. 3, pp.
–210.
H. Li and O. Chutatape, "Automated feature extraction in color retinal
images by a model based Approach”, IEEE Trans. Biomed. Eng., Feb.
, vol. 51, no. 2, pp. 246-254.
F. Zana and J. Klein, “Segmentation of vessel-like patterns using
mathematical morphology and curvature evaluation”, IEEE Trans. Image
Process., Jul. 2001, vol. 10, no. 7, pp. 1010–1019.
J. Lowell, A. Hunter, D. Steel, A. Basu, R. Ryder, and R. L. Kennedy,
“Measurement of retinal vessel widths from fundus images based on 2-d
modeling”, IEEE Trans. Med. Imag., Oct. 2004, vol. 23, no. 10, pp.
–1204.
L. Wang, A. Bhalerao, and R. Wilson, “Analysis of retinal vasculature
using a multiresolution hermite model”, IEEE Trans. Med. Imag., Feb.
, vol. 26, no. 2, pp. 137–152.
M. Sotka, C. V. Stewart, "Retinal vessel centerline extraction using
multiscale matched filters, confidence and edge measures”, IEEE Trans.
Med. Imag., Dec. 2006, vol. 25, no. 12, pp. 1531-1545.
X. Jiang and D. Mojon, “Adaptive local thresholding by verification
based multithreshold probing with application to vessel detection in
retinal images”, IEEE Trans. Pattern Anal. Mach. Intell., Jan. 2003, vol.
, no. 1, pp. 131–137.
O. Chutatape, L. Zheng, and S. Krishnan, “Retinal blood vessel detection
and tracking by matched gaussian and kalman filters”, in Proc. IEEE Int.
Conf. Emg. Bio. Soc., 1998, vol. 20, pp. 3144–3149.
A. Can, H. Shen, J. N. Turner, H. L. Tanenbaum, and B. Roysam, “Rapid
automated tracing and feature extraction from retinal fundus images using
direct exploratory algorithms”, IEEE Trans. Inf. Technol. Biomed., Jun.
, vol. 3( 1), pp. 1–15.
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., Mar. 2001, vol. 5, no. 1, pp. 77–91.
V. J. Soares, J. J. Leandro, R. M. J. Cesar, F. H. Jelinek, and M. J. Cree,
“Retinal vessel segmentation using the 2-d gabor wavelet and supervised
classification”, IEEE Trans. Med. Imag., Sep. 2006, vol. 25, no. 9, pp.
–1222,.
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., Apr. 2004, vol. 23, no. 4, pp. 501-509.
C. A. Lupas, D. Tegolo, and E. Trucco, “FABC: Retinal vessel
segmentation using adaboost”, IEEE Trans. on Info. Tech. in Biomed.,
Sept. 2010, vol. 14, no. 5.
M. C. Morrone, J. R. Ross, D. C. Burr, and R. A. Owens, “Mach bands are
phase dependent”, Nature, Nov. 1986, vol. 324, no. 6094, pp. 250–253.
M. C. Morrone and R. A. Owens, “Feature detection from local energy”,
, Patt. Reco. Lett., vol. 6, pp. 303–313.
P.D. Kovesi, “Image features from phase congruency”, Videre: Jour. of
Comp. Vis. Res., summer 1999, vol. 1, no. 3, The MIT Press.
J. Morlet, G. Arens, E. Fourgeau, and D. Giard, “Wave propagation and
sampling theory - Part II: sampling theory and complex waves”,
Geophysics, Feb. 1982, vol. 47, no. 2, pp 222–236.
Y. Yu and S. T. Acton, “Speckle reducing anisotropic diffusion”, IEEE
Trans. on Im. Proc., Nov 2002, vol. 11, no. 11.
P. D. Wellner, “Adaptive Thresholding for the DigitalDesk”, Technical
Report EPC-1993-110, Rank Xerox Research Centre, Cambridge
Laboratory, 61 Regent Street, Cambridge CB2 1AB.
P.D. Kovesi, MATLAB functions for computer vision and image analysis
(1996-2003) http://www.csse.uwa.edu.au/»pk/Research/MatlabFns/.
T. Walter, P. Massin, A. Erginay, R. Ordonez, C. Jeulin, and J. C. Klein,
“Automatic detection of microaneurysms in color fundus images”, Med.
Image Anal., 2007, vol. 11, pp. 555–566.
D. Marin, A. Aquino, M. E Gegundez-Arias, J. M. Bravo, “New
supervised method for blood vessel segmentation in retinal images by
using gray-level and moment invariants-based features”, IEEE Trans. on
Med. Imag., Jan 2011, vol. 30, no. 1, pp 146-154.
A. Hoover, STARE database [Online]. Available:
http://www.ces.clemson.edu/-ahoover/stare
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution 3.0 License.