A Similarity Function for Global Quality Assessment of Retinal Vessel Segmentations

Retinal vascularity assessment plays an important role in diagnosis of ophthalmic pathologies. The employment of digital images for this purpose makes possible a computerized approach and has motivated development of many methods for automated vascular tree segmentation. Metrics based on contingency tables for binary classification have been widely used for evaluating performance of these algorithms and, concretely, the accuracy has been mostly used as measure of global performance in this topic. However, this metric shows very poor matching with human perception as well as other notable deficiencies. Here, a new similarity function for measuring quality of retinal vessel segmentations is proposed. This similarity function is based on characterizing the vascular tree as a connected structure with a measurable area and length. Tests made indicate that this new approach shows better behaviour than the current one does. Generalizing, this concept of measuring descriptive properties may be used for designing functions for measuring more successfully segmentation quality of other complex structures.

• Arturo Aquino
• Manuel Emilio Gegundez
• Jose Manuel Bravo
• Diego Marin

• Retinal vessel segmentation
• quality assessment
• performanceevaluation
• similarity function.

[1] T. Walter and J. C. Klein, "Segmentation of color fundus images of
the human retina: Detection of the optic disc and the vascular tree
using morphological techniques," in Medical Data Analysis, J. Crespo,
V. Maojo, and F. Martin, Eds. Berlin, Germany: Springer-Verlag, 2001,
pp. 282-287. ser. Lecture Notes in Computer Science.
[2] F. Zana and J. C. Klein, "Segmentation of vessel-like patterns using
mathematical morphology and curvature evaluation," IEEE Trans. Image
Processing, vol. 10, pp. 1010-1019, 2001.
[3] C. Heneghan, J. Flynn, M. OKeefe, and M. Cahill, "Characterization of
changes in blood vessel width and tortuosity in retinopathy of prematurity
using image analysis," Med. Image Anal., vol. 6, pp. 407-429, 2002.
[4] A. M. Mendonc┬©a and A. Campilho, "Segmentation of Retinal Blood
Vessels by Combining the Detection of Centerlines and Morphological
Reconstruction," IEEE Trans. Med. Imag., vol. 25, no. 9, pp. 1200-1213,
Sept. 2006.
[5] 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., vol. 8, pp. 263-269, 1989.
[6] A. Hoover, V. Kouznetsova, and M. Goldbaum, "Locating blood vessels
in retinal images by piecewise threshold probing of a matched filter
response," IEEE Trans. Med. Imag., vol. 19, pp. 203-210, 2000.
[7] L. Gang, O. Chutatape, and S. M. Krishnan, "Detection and measurement
of retinal vessels in fundus images using amplitude modified second-order
Gaussian filter," IEEE Trans. Biomed. Eng., vol. 49, pp. 168-172, 2002.
[8] M. Al-Rawi and H. Karajeh, "Genetic algorithm matched filter optimization
for automated detection of blood vessels from digital retinal images,"
Comput. Methods Programs Biomed. vol. 87, pp. 248253, 2007.
[9] M. Al-Rawi, M. Qutaishat, and M. Arrar, "An improved matched filter
for blood vessel detection of digital retinal images," Comput. Biol. Med.
vol. 37, pp. 262267, 2007.
[10] M. G. Cinsdikici and D. Ayd─▒n, "Detection of blood vessels in ophthalmoscope
images using MF/ant (matched filter/ant colony) algorithm,"
Comput. Methods Programs Biomed., vol. 96, no. 2, pp. 85-95, Nov. 2009.
[11] G. G. Gardner, D. Keating, T. H. Williamson, and A. T. Elliott,
"Automatic detection of diabetic retinopathy using an artificial neural
network: A screening tool," Br. J. Ophthalmol., vol. 80, pp. 940-944,
1996.
[12] C. Sinthanayothin, J. F. Boyce, H. L. Cook, and T. H. Williamson,
"Automated localisation of the optic disc, fovea and retinal blood vessels
from digital colour fundus images," Br. J. Ophtalmol., vol. 83, pp. 902-
910, 1999.
[13] M. Niemeijer, J. Staal, B. van Ginneken, M. Loog, and M. D. Abramoff,
"Comparative study of retinal vessel segmentation methods on a new
publicly available database," in SPIE Med. Imag., J.M. Fitzpatrick and
M. Sonka, Eds., 2004, vol. 5370, pp. 648-656.
[14] J. Staal, M. D. Abr`amoff, M. Niemeijer, M. A. Viergever, and B. van
Ginneken, "Ridge based vessel segmentation in color images of the
retina," IEEE Trans. Med. Imag., vol. 23, pp. 501-509, 2004.
[15] J. V. B. Soares, J. J. G. Leandro, R. M. Cesar Jr., H. F. Jelinek, and
M. J. Cree, "Retinal vessel segmentation using the 2D Gabor wavelet and
supervised classification," IEEE Trans. Med. Imag., vol. 25, pp. 1214-
1222, 2006.
[16] E. Ricci and R. Perfetti, "Retinal Blood Vessel Segmentation Using Line
Operators and Support Vector Classification," IEEE Trans. Med. Imag.,
vol. 26, no. 10, pp. 1357-1365, Oct. 2007
[17] X. Jiang and D. Mojon, "Adaptive Local Thresholding by Verification-
Based Multithreshold Probing with Application to Vessel Detection in
Retinal Images," IEEE Trans. Pattern Analy. Mach. Intell., vol. 25, no.
1, pp. 131-137, Jan. 2003.
[18] T. Fawcett, "An introduction to ROC analysis," Pattern Recognition
Letters, vol. 27, no. 8, pp. 861-874, June 2006.
[19] J. Serra, Image Analysis and Mathematical Morphology. London: Academic
Press, 1982, vol. 1.
[20] Research section, digital retinal image for vessel extraction (DRIVE)
database University Medical Center Utrecht, Image Sciences Institute,
Utrecht, The Netherlands (Online). Available: http://www.isi.uu.nl/Research/
Databases/DRIVE