The automatic analysis of retinal blood vessels plays an important role in the computer-aided diagnosis. In this paper, we introduce a probabilistic tracking-based method for automatic vessel segmentation in retinal images. We take into account vessel edge detection on the whole retinal image and handle different vessel structures. During the tracking process, a Bayesian method with maximum a posteriori (MAP) as criterion is used to detect vessel edge points. Experimental evaluations of the tracking algorithm are performed on real retinal images from three publicly available databases: STARE (Hoover et al., 2000), DRIVE (Staal et al., 2004), and REVIEW (Al-Diri et al., 2008 and 2009). We got high accuracy in vessel segmentation, width measurements, and vessel structure identification. The sensitivity and specificity on STARE are 0.7248 and 0.9666, respectively. On DRIVE, the sensitivity is 0.6522 and the specificity is up to 0.9710. 1. Introduction Automatic vessel segmentation in medical images is a very important task in many clinical investigations. In ophthalmology, the early diagnosis of several pathologies such as arterial hypertension, arteriosclerosis, diabetic retinopathy, cardiovascular disease, and stroke [1, 2] could be achieved by analyzing changes in blood vessel patterns such as tortuosity, bifurcation, and variation of vessel width on retinal images. Early detection and characterization of retinal blood vessels are needed for a better and effective treatment of diseases. Hence, computer-aided detection and analysis of retinal images could help doctors, allowing them to use a quantitative tool for a better diagnosis, especially when analyzing a huge amount of retinal images in screening programs. Many methods for blood vessel detection on retinal images have been reported in the literature [3–5]. These techniques can be roughly classified into pixel-based methods [6–14], model-based methods [15–21], and tracking-based approaches [22–29], respectively. Pixel-based approaches consist in convolving the image with a spatial filter and then assigning each pixel to background or vessel region, according to the result of a second processing step such as thresholding or morphological operation. Chaudhuri et al. [8] used 2D Gaussian kernels with 12 orientations, retaining the maximum response. Hoover et al. [6] improved this technique by computing local features to assign regions to vessel or background. A multithreshold scheme was used by Jiang and Mojon [9], whereas Sofka and Stewart [10] presented a multiscale matched filter. Zana and Klein
References
[1]
A. M. Mendon？a and A. Campilho, “Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction,” IEEE Transactions on Medical Imaging, vol. 25, no. 9, pp. 1200–1213, 2006.
[2]
B. Fang, W. Hsu, and M. L. Lee, “On the detection of retinal vessels in fundus images,” 2003, http://hdl.handle.net/1721.1/3675.
[3]
C. Kirbas and F. Quek, “A review of vessel extraction techniques and algorithms,” ACM Computing Surveys, vol. 36, no. 2, pp. 81–121, 2004.
[4]
D. Lesage, E. D. Angelini, I. Bloch, and G. Funka-Lea, “A review of 3D vessel lumen segmentation techniques: models, features and extraction schemes,” Medical Image Analysis, vol. 13, no. 6, pp. 819–845, 2009.
[5]
M. M. Fraz, P. Remagnino, A. Hoppe et al., “Blood vessel segmentation methodologies in retinal images—a survey,” Computer Methods and Programs in Biomedicine, vol. 108, pp. 407–433, 2012.
[6]
A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” IEEE Transactions on Medical Imaging, vol. 19, no. 3, pp. 203–210, 2000.
[7]
J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” IEEE Transactions on Medical Imaging, vol. 23, no. 4, pp. 501–509, 2004.
[8]
S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters,” IEEE Transactions on Medical Imaging, vol. 8, no. 3, pp. 263–269, 1989.
[9]
X. Jiang and D. Mojon, “Adaptive local thresholding by verification-based multithreshold probing with application to vessel detection in retinal images,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 1, pp. 131–137, 2003.
[10]
M. Sofka and C. V. Stewart, “Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures,” IEEE Transactions on Medical Imaging, vol. 25, no. 12, pp. 1531–1546, 2006.
[11]
F. Zana and J. C. Klein, “Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation,” IEEE Transactions on Image Processing, vol. 10, no. 7, pp. 1010–1019, 2001.
[12]
M. Niemeijer, J. Staal, B. van Ginneken, M. Loog, and M. D. Abràmoff, “Comparative study of retinal vessel segmentation methods on a new publicly available database,” in Medical Imaging 2004: Image Processing, vol. 5370 of Proceedings of SPIE, pp. 648–656, San Diego, Calif, USA, February 2004.
[13]
J. V. B. Soares, J. J. G. Leandro, R. M. Cesar Jr., H. F. Jelinek, and M. J. Cree, “Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification,” IEEE Transactions on Medical Imaging, vol. 25, no. 9, pp. 1214–1222, 2006.
[14]
E. Ricci and R. Perfetti, “Retinal blood vessel segmentation using line operators and support vector classification,” IEEE Transactions on Medical Imaging, vol. 26, no. 10, pp. 1357–1365, 2007.
[15]
S. Kozerke, R. Botnar, S. Oyre, M. B. Scheidegger, E. M. Pedersen, and P. Boesiger, “Automatic vessel segmentation using active contours in cine phase contrast flow measurements,” Journal of Magnetic Resonance Imaging, vol. 10, pp. 41–51, 1999.
[16]
A. Gooya, H. Liao, K. Matsumiya, K. Masamune, Y. Masutani, and T. Dohi, “A variational method for geometric regularization of vascular segmentation in medical images,” IEEE Transactions on Image Processing, vol. 17, no. 8, pp. 1295–1312, 2008.
[17]
A. Vasilevskiy and K. Siddiqi, “Flux maximizing geometric flows,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 12, pp. 1565–1578, 2002.
[18]
M. W. K. Law and A. C. S. Chung, “Efficient implementation for spherical flux computation and its application to vascular segmentation,” IEEE Transactions on Image Processing, vol. 18, no. 3, pp. 596–612, 2009.
[19]
T. Kayikcioglu and S. Mitra, “A new method for estimating dimensions and 3-d reconstruction of coronary arterial trees from biplane angiograms,” in Proceedings of 6th Annual IEEE Symposium on Computer-Based Medical Systems, pp. 153–158, Jun 1993.
[20]
Y. Sato, T. Araki, M. Hanayama, H. Naito, and S. Tamura, “A viewpoint determination system for stenosis diagnosis and quantification in coronary angiographie image acquisition,” IEEE Transactions on Medical Imaging, vol. 17, no. 1, pp. 121–137, 1998.
[21]
L. Wang, A. Bhalerao, and R. Wilson, “Analysis of retinal vasculature using a multiresolution hermite model,” IEEE Transactions on Medical Imaging, vol. 26, no. 2, pp. 137–152, 2007.
[22]
Y. A. Tolias and S. M. Panas, “A fuzzy vessel tracking algorithm for retinal images based on fuzzy clustering,” IEEE Transactions on Medical Imaging, vol. 17, no. 2, pp. 263–273, 1998.
[23]
A. H. 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 Transactions on Information Technology in Biomedicine, vol. 3, no. 2, pp. 125–138, 1999.
[24]
P. Zou, P. Chan, and P. Rockett, “A model-based consecutive scanline tracking method for extracting vascular networks from 2-D digital subtraction angiograms,” IEEE Transactions on Medical Imaging, vol. 28, no. 2, pp. 241–249, 2009.
[25]
O. Chutatape, L. Zheng, and S. Krishnan, “Retinal blood vessel detection and tracking by matched gaussian and kalman filters,” in Proceedings of the 20th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '98), pp. 3144–3149, 1998.
[26]
E. Grisan, A. Pesce, A. Giani, M. Foracchia, and A. Ruggeri, “A new tracking system for the robust extraction of retinal vessel structure,” in Proceedings of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '04), pp. 1620–1623, San Francisco, Calif, USA, September 2004.
[27]
J. Ng, S. T. Clay, S. A. Barman et al., “Maximum likelihood estimation of vessel parameters from scale space analysis,” Image and Vision Computing, vol. 28, no. 1, pp. 55–63, 2010.
[28]
T. Yedidya and R. Hartley, “Tracking of blood vessels in retinal images using kalman filter,” in Proceedings of the Digital Image Computing: Techniques and Applications (DICTA '08), pp. 52–58, Canberra, Australia, December 2008.
[29]
M. Vlachos and E. Dermatas, “Multi-scale retinal vessel segmentation using line tracking,” Computerized Medical Imaging and Graphics, vol. 34, no. 3, pp. 213–227, 2010.
[30]
B. Al-Diri, A. Hunter, and D. Steel, “An active contour model for segmenting and measuring retinal vessels,” IEEE Transactions on Medical Imaging, vol. 28, no. 9, pp. 1488–1497, 2009.
[31]
M. E. Martínez-Pérez, A. D. Hughes, A. V. Stanton, S. A. Thom, A. A. Bharath, and K. H. Parker, “Retinal blood vessel segmentation by means of scale-space analysis and region growing,” in Proceedings of the Medical Image Computing and Computer-Assisted Intervention (MICCAI '99), pp. 90–97, 1999.
[32]
S. Zhou, W. Chen, Z. Zhang, and J. Yang, “Automatic segmentation of coronary angiograms based on probabilistic tracking,” in Proceedings of the 3rd International Conference on Bioinformatics and Biomedical Engineering (ICBBE '09), pp. 1–4, Beijing, China, June 2009.
[33]
C. Florin, N. Paragios, and J. Williams, “Particle filters, a quasi-monte carlo solution for segmentation of coronaries,” in Proceedings Medical Image Computing and Computer-Assisted Intervention (MICCAI '05), pp. 246–253, 2005.
[34]
K. A. Al-Kofahi, S. Lasek, D. H. Szarowski et al., “Rapid automated three-dimensional tracing of neurons from confocal image stacks,” IEEE Transactions on Information Technology in Biomedicine, vol. 6, no. 2, pp. 171–187, 2002.
[35]
M. Adel, A. Moussaoui, M. Rasigni, S. Bourennane, and L. Hamami, “Statistical-based tracking technique for linear structures detection: application to vessel segmentation in medical images,” IEEE Signal Processing Letters, vol. 17, pp. 555–558, 2010.
[36]
Y. Yin, M. Adel, and S. Bourennane, “Retinal vessel segmentation using a probabilistic tracking method,” Pattern Recognition, vol. 45, no. 4, pp. 1235–1244, 2012.
[37]
P. H. Gregson, Z. Shen, R. C. Scott, and V. Kozousek, “Automated grading of venous beading,” Computers and Biomedical Research, vol. 28, no. 4, pp. 291–304, 1995.
[38]
O. Brinchmann-Hansen and H. Heier, “Theoretical relations between light streak characteristics and optical properties of retinal vessels,” Acta Ophthalmologica, vol. 64, no. 179, pp. 33–37, 1986.
[39]
L. Zhou, M. S. Rzeszotarski, L. J. Singerman, and J. M. Chokreff, “Detection and quantification of retinopathy using digital angiograms,” IEEE Transactions on Medical Imaging, vol. 13, no. 4, pp. 619–626, 1994.
[40]
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 Transactions on Medical Imaging, vol. 23, no. 10, pp. 1196–1204, 2004.
[41]
A. R. Rao and R. C. Jain, “Computerized flow field analysis: oriented texture fields,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 14, pp. 693–709, 1992.
[42]
M. V. Iba？ez and A. Simó, “Bayesian detection of the fovea in eye fundus angiographies,” Pattern Recognition Letters, vol. 20, no. 2, pp. 229–240, 1999.
[43]
B. Al-Diri, A. Hunter, D. Steel, M. Habib, T. Hudaib, and S. Berry, “Review—a reference data set for retinal vessel profiles,” in Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS '08), pp. 2262–2265, Vancouver, Canada, August 2008.
