American Institute of Mathematical Sciences

Advanced
May  2014, 8(2): 459-473. doi: 10.3934/ipi.2014.8.459

Retinal vessel segmentation using a finite element based binary level set method

Zhenlin Guo 1, , Ping Lin 2, , Guangrong Ji 3, and  Yangfan Wang 4,

1. 

Department of Mathematics and Mechanics, University of Science and Technology Beijing (USTB), Beijing, 100083, China
2. 

Division of Mathematics, University of Dundee, Dundee, DD1 4HN, United Kingdom
3. 

College of Information Science and Engineering, Ocean University of China, Qingdao, 266071, China
4. 

College of Marine Life Science, Ocean University of China, Qingdao, 266071, China

Received  July 2012 Revised  October 2013 Published  May 2014

In this paper we combine a few techniques to label blood vessels in the matched filter (MF) response image by using a finite element based binary level set method. An operator-splitting method is applied to numerically solve the Euler-Lagrange equation from minimizing an energy functional. Unlike the traditional MF methods, where a threshold is difficult to be selected, our method can automatically get more precise blood vessel segmentation using an enhanced edge information. In order to demonstrate the good performance, we compare our method with a few other methods when they are applied to a publicly available standard database of coloured images (with manual segmentations available too).
Keywords: operator-splitting., Retinal vessel segmentation, binary level set method.
Mathematics Subject Classification: Primary: 65M60, 65J22; Secondary: 94A08, 53C3.
Citation: Zhenlin Guo, Ping Lin, Guangrong Ji, Yangfan Wang. Retinal vessel segmentation using a finite element based binary level set method. Inverse Problems & Imaging, 2014, 8 (2) : 459-473. doi: 10.3934/ipi.2014.8.459
References:
[1]

X. Cai, R. Chan, S. Morigi and F. Sgallari, Framelet-based algorithm for segmentation of tubular structures, Lecture Notes in Computer Science, 6667 (2012), 411-422. doi: 10.1007/978-3-642-24785-9_35.  Google Scholar

[2]

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. Inform. Technol. Biomed., 3 (1999), 125-138. Google Scholar

[3]

V. Caselles, F. Catte, T. Coll and F. Dibos, A geometric model for active contours in image processing, Numer. Math., 66 (1993), 1-31. doi: 10.1007/BF01385685.  Google Scholar

[4]

T. Chan and L. Vese, Active contours without edges, IEEE Image Proc., 10 (2001), 266-277. doi: 10.1109/83.902291.  Google Scholar

[5]

S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson and M. Goldbaum, Detection of blood vessel in retinal images using two-dimensional matched filter, IEEE Trans. Med. Imag., 8 (1989), 263-269. doi: 10.1109/42.34715.  Google Scholar

[6]

N. Cheung, K. Donaghue, G. Liew, L. Rogers, J. Wang, S. Lim, A. Jenkins, W. Hsu, L. Lee and T. Wong, Quantitative Assessment of Early Diabetic Retinopathy Using Fractal Analysis, Diabetes Care, 32 (2009), 106-110. Google Scholar

[7]

J. Chen and A. Amini, Quantifying 3D vascular structures in MRA images using hybrid PDE and geometric deformable models, IEEE Trans. Med. Imag., 10 (2004), 1251-1262. Google Scholar

[8]

O. Chutatape, L. Zheng and S. Krishman, Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters, in Proc. IEEE Int. Conf. Eng. Biol. Soc., 6 (1998), 3144-3149. doi: 10.1109/IEMBS.1998.746160.  Google Scholar

[9]

B. Dong, A. Chien and Z. Shen, Frame based segmentation for medical images, Commun.Math. Sci., 9 (2011), 551-559. doi: 10.4310/CMS.2011.v9.n2.a10.  Google Scholar

[10]

A. F. Frangi, W. J. Niessen, R. M. Hoogeveen, T. van Walsum and M. A. Viergever, Model-based quantitation of 3-D magnetic resonance angiographic images, IEEE Trans. Med. Imag. , 18 (1999), 946-956. doi: 10.1109/42.811279.  Google Scholar

[11]

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., 49 (2002), 168-172. Google Scholar

[12]

R. Glowinski, P. Lin and X. Pan, An operator-splitting method for a liquid crystal model, Comp Phys. Comm., 152 (2003), 242-252. doi: 10.1016/S0010-4655(02)00823-8.  Google Scholar

[13]

R. Glowinski, P. Lin and X. Pan, A three-stage operator-splitting/finite element method for the numerical simulation of liquid crystal flow, Int. J. Numer. Anal. Mod., 6 (2009), 440-454.  Google Scholar

[14]

R. Glowinski and P. Tallec, Augmented Lagrangian and Operator-Splitting Methods in Nonlinear Mechanics, SIAM, Philadelphia, PA, 1989. doi: 10.1137/1.9781611970838.  Google Scholar

[15]

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., 19 (2000), 203-210. Google Scholar

[16]

J. Hua, P. Lin, C. Liu and Q. Wang, Energy law preserving C0 finite element schemes for phase field models in two-phase flow computations, J. Comput. Phys., 230 (2011), 7115-7131. doi: 10.1016/j.jcp.2011.05.013.  Google Scholar

[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 Anal. Mach. Intell., 25 (2003), 131-137. Google Scholar

[18]

M. Kass, A. Witkin and D. Terzopoulos, Snakes: Active contour models, Int. J. Comput. Vis., 1 (1987), 321-331. doi: 10.1007/BF00133570.  Google Scholar

[19]

C. Kirbas and F. K. H. Quek, A review of vessel extraction techniques and algorithms, ACM Comput. Surv., 36 (2004), 81-121. doi: 10.1145/1031120.1031121.  Google Scholar

[20]

J. Lie, M. Lysaker and X. Tai, A variant of the levelset method and applications to image segmentation, UCLA CAM 03- 50, (2003). Google Scholar

[21]

J. Lie, M. Lysaker and X. Tai, A binary level set metod and some application to image processing, UCLA CAM 04-31, (2004). Google Scholar

[22]

J. Lie, M. Lysaker and X. Tai, Piecewise constant level set methods and image segmentation. In Scale Space and PDE Methods in Computer Vision, Lectures notes in Computer Sciences, 3459 (2005), 573-584. Springer. Google Scholar

[23]

P. Lin and C. Liu, Simulation of singularity dynamics in liquid crystal flows: a C0 finite element approach, J. Comp. Phys., 215 (2006), 348-362. doi: 10.1016/j.jcp.2005.10.027.  Google Scholar

[24]

C. Liu and J. Shen, A phase field model for the mixture of two incompressible fluids and its approximation by a Fourier-Spectral method, Phys. D, 179 (2003), 211-228. doi: 10.1016/S0167-2789(03)00030-7.  Google Scholar

[25]

L. M. Lorigo, O. Faugeras, W. E. L. Grimson, R. Keriven, R. Kikins, A. Nabavi and C.-F. Westin, CURVES: Curve evolution for vessel segmentation, Med. Image. Anal., 5 (2001), 195-206. doi: 10.1016/S1361-8415(01)00040-8.  Google Scholar

[26]

C. Lupascu, D. Tegolo and E. Trucco, FABC: Retinal vessel segmentation using adaBoost, IEEE Trans. Inf. Technol. Biomed., 14 (2010), 1267-1274. doi: 10.1109/TITB.2010.2052282.  Google Scholar

[27]

T. McInerney and D. Terzopoulos, T-snakes: Topology adaptive snakes, Med. Imag. Anal. , 4 (2000), 73-91. doi: 10.1016/S1361-8415(00)00008-6.  Google Scholar

[28]

T. McInerney and D. Terzopoulos, Deformable models in medical image analysis: A survey, Med. Image Anal., 1 (1996), 91-108. doi: 10.1016/S1361-8415(96)80007-7.  Google Scholar

[29]

A. M. Mendonca and A. Campilho, Segmentation of Retinal Blood Vessels by Combining the Detection of Centerlines and Morphological Reconstruction, IEEE Trans. Med. Imag., 25 (2006), 1200-1213. doi: 10.1109/TMI.2006.879955.  Google Scholar

[30]

C. E. Metz, Basic principles of ROC analysis, Seminars Nucl. Med., 8 (1978), 283-298. doi: 10.1016/S0001-2998(78)80014-2.  Google Scholar

[31]

D. Mumford and J. Shah, Optimal approximation by piecewise smooth functions and associated variational problems, Commun. Pure Appl. Math., 42 (1989), 577-685. doi: 10.1002/cpa.3160420503.  Google Scholar

[32]

M. Niemeijer, J. Staal, B. Ginneken, M. Long and M. D. Abramoff, Comparative study of retinal vessel segmentation methods on a new publicly available database, Proc. SPIE Med. Imag., 5370 (2004), 648-656. doi: 10.1117/12.535349.  Google Scholar

[33]

S. Osher and J. A. Sethian, Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi Formulation, J. Comput. Phys., 79 (1988), 12-49. doi: 10.1016/0021-9991(88)90002-2.  Google Scholar

[34]

Q. Sheng, Recent trends in splitting, adaptive and hybrid numerical methods for differential equations, Neural, Parallel and Sci. Comput., 16 (2008), 283-301.  Google Scholar

[35]

C. Sinthanayothin, J. F. Boyce, T. H. Williamson, H. L. Cook, E. Mensah, S. Lal and D. Usher, Automated detection of diabetic retinopathy on digital fundus images, Diabetic Med., 19 (2002), 105-112. doi: 10.1046/j.1464-5491.2002.00613.x.  Google Scholar

[36]

J. Soares, J. Leandro, J. Cesar, H. Jelinek and M. Cree, Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification, IEEE Trans. Med. Imag., 25 (2006), 1214-1222. doi: 10.1109/TMI.2006.879967.  Google Scholar

[37]

J. Staal, M. Abramoff, M. Viergever and B. Ginneken, Ridge based vessel segmentation in color images of the retina, IEEE Trans. Med. Imag., 23 (2004), 501-509. doi: 10.1109/TMI.2004.825627.  Google Scholar

[38]

K. Sum and P. Cheung, Vessel extraction under non-uniform illumination: A level set approach, IEEE Trans. Biomed. Eng., 55 (2008), 358-360. doi: 10.1109/TBME.2007.896587.  Google Scholar

[39]

X. Tai, O. Christiansen, P. Lin and I. Skjaelaaen, A remark on the MBO scheme and some piecewise constant level set methods, Int. J. Comput. Vis., 73 (2007), 61-76. Google Scholar

[40]

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, 2199 (2001), 282-287. ser. Lecture Notes in Computer Science. doi: 10.1007/3-540-45497-7_43.  Google Scholar

[41]

L. Wang, A. Bhalerao and R. Wilson, Analysis of retinal vasculature using a multiresolution hermite model, IEEE Trans. Med. Imag., 26 (2007), 137-152. doi: 10.1109/TMI.2006.889732.  Google Scholar

[42]

Y. Wang, G. Ji, P. Lin and E. Trucco, Retinal vessel segmentation using matched filter with multiwavelet kernels and multiscale hierachical decomposition, Pattern Recog., 46 (2013), 2117-2133. Google Scholar

[43]

C. Wu and X. Tai, Augmented Lagrangian method, Dual methods and Split-Bregman Iterations for ROF, vectorial TV and higher order models, SIAM J. Imag. Sci., 3 (2010), 300-339. doi: 10.1137/090767558.  Google Scholar

[44]

F. Zana and J. C. Klein, Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation, IEEE Trans. Imag. Proc., 10 (2001), 1010-1019. doi: 10.1109/83.931095.  Google Scholar

[45]

D. Zonoobi, A. Kassim and W. Shen, Vasculature segmentation in MRA images using gradient compensated geodesic active contours, J. Sign. Process. Syst., 54 (2009), 171-181. doi: 10.1007/s11265-008-0216-4.  Google Scholar

show all references

References:
[1]

X. Cai, R. Chan, S. Morigi and F. Sgallari, Framelet-based algorithm for segmentation of tubular structures, Lecture Notes in Computer Science, 6667 (2012), 411-422. doi: 10.1007/978-3-642-24785-9_35.  Google Scholar

[2]

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. Inform. Technol. Biomed., 3 (1999), 125-138. Google Scholar

[3]

V. Caselles, F. Catte, T. Coll and F. Dibos, A geometric model for active contours in image processing, Numer. Math., 66 (1993), 1-31. doi: 10.1007/BF01385685.  Google Scholar

[4]

T. Chan and L. Vese, Active contours without edges, IEEE Image Proc., 10 (2001), 266-277. doi: 10.1109/83.902291.  Google Scholar

[5]

S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson and M. Goldbaum, Detection of blood vessel in retinal images using two-dimensional matched filter, IEEE Trans. Med. Imag., 8 (1989), 263-269. doi: 10.1109/42.34715.  Google Scholar

[6]

N. Cheung, K. Donaghue, G. Liew, L. Rogers, J. Wang, S. Lim, A. Jenkins, W. Hsu, L. Lee and T. Wong, Quantitative Assessment of Early Diabetic Retinopathy Using Fractal Analysis, Diabetes Care, 32 (2009), 106-110. Google Scholar

[7]

J. Chen and A. Amini, Quantifying 3D vascular structures in MRA images using hybrid PDE and geometric deformable models, IEEE Trans. Med. Imag., 10 (2004), 1251-1262. Google Scholar

[8]

O. Chutatape, L. Zheng and S. Krishman, Retinal blood vessel detection and tracking by matched Gaussian and Kalman filters, in Proc. IEEE Int. Conf. Eng. Biol. Soc., 6 (1998), 3144-3149. doi: 10.1109/IEMBS.1998.746160.  Google Scholar

[9]

B. Dong, A. Chien and Z. Shen, Frame based segmentation for medical images, Commun.Math. Sci., 9 (2011), 551-559. doi: 10.4310/CMS.2011.v9.n2.a10.  Google Scholar

[10]

A. F. Frangi, W. J. Niessen, R. M. Hoogeveen, T. van Walsum and M. A. Viergever, Model-based quantitation of 3-D magnetic resonance angiographic images, IEEE Trans. Med. Imag. , 18 (1999), 946-956. doi: 10.1109/42.811279.  Google Scholar

[11]

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., 49 (2002), 168-172. Google Scholar

[12]

R. Glowinski, P. Lin and X. Pan, An operator-splitting method for a liquid crystal model, Comp Phys. Comm., 152 (2003), 242-252. doi: 10.1016/S0010-4655(02)00823-8.  Google Scholar

[13]

R. Glowinski, P. Lin and X. Pan, A three-stage operator-splitting/finite element method for the numerical simulation of liquid crystal flow, Int. J. Numer. Anal. Mod., 6 (2009), 440-454.  Google Scholar

[14]

R. Glowinski and P. Tallec, Augmented Lagrangian and Operator-Splitting Methods in Nonlinear Mechanics, SIAM, Philadelphia, PA, 1989. doi: 10.1137/1.9781611970838.  Google Scholar

[15]

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., 19 (2000), 203-210. Google Scholar

[16]

J. Hua, P. Lin, C. Liu and Q. Wang, Energy law preserving C0 finite element schemes for phase field models in two-phase flow computations, J. Comput. Phys., 230 (2011), 7115-7131. doi: 10.1016/j.jcp.2011.05.013.  Google Scholar

[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 Anal. Mach. Intell., 25 (2003), 131-137. Google Scholar

[18]

M. Kass, A. Witkin and D. Terzopoulos, Snakes: Active contour models, Int. J. Comput. Vis., 1 (1987), 321-331. doi: 10.1007/BF00133570.  Google Scholar

[19]

C. Kirbas and F. K. H. Quek, A review of vessel extraction techniques and algorithms, ACM Comput. Surv., 36 (2004), 81-121. doi: 10.1145/1031120.1031121.  Google Scholar

[20]

J. Lie, M. Lysaker and X. Tai, A variant of the levelset method and applications to image segmentation, UCLA CAM 03- 50, (2003). Google Scholar

[21]

J. Lie, M. Lysaker and X. Tai, A binary level set metod and some application to image processing, UCLA CAM 04-31, (2004). Google Scholar

[22]

J. Lie, M. Lysaker and X. Tai, Piecewise constant level set methods and image segmentation. In Scale Space and PDE Methods in Computer Vision, Lectures notes in Computer Sciences, 3459 (2005), 573-584. Springer. Google Scholar

[23]

P. Lin and C. Liu, Simulation of singularity dynamics in liquid crystal flows: a C0 finite element approach, J. Comp. Phys., 215 (2006), 348-362. doi: 10.1016/j.jcp.2005.10.027.  Google Scholar

[24]

C. Liu and J. Shen, A phase field model for the mixture of two incompressible fluids and its approximation by a Fourier-Spectral method, Phys. D, 179 (2003), 211-228. doi: 10.1016/S0167-2789(03)00030-7.  Google Scholar

[25]

L. M. Lorigo, O. Faugeras, W. E. L. Grimson, R. Keriven, R. Kikins, A. Nabavi and C.-F. Westin, CURVES: Curve evolution for vessel segmentation, Med. Image. Anal., 5 (2001), 195-206. doi: 10.1016/S1361-8415(01)00040-8.  Google Scholar

[26]

C. Lupascu, D. Tegolo and E. Trucco, FABC: Retinal vessel segmentation using adaBoost, IEEE Trans. Inf. Technol. Biomed., 14 (2010), 1267-1274. doi: 10.1109/TITB.2010.2052282.  Google Scholar

[27]

T. McInerney and D. Terzopoulos, T-snakes: Topology adaptive snakes, Med. Imag. Anal. , 4 (2000), 73-91. doi: 10.1016/S1361-8415(00)00008-6.  Google Scholar

[28]

T. McInerney and D. Terzopoulos, Deformable models in medical image analysis: A survey, Med. Image Anal., 1 (1996), 91-108. doi: 10.1016/S1361-8415(96)80007-7.  Google Scholar

[29]

A. M. Mendonca and A. Campilho, Segmentation of Retinal Blood Vessels by Combining the Detection of Centerlines and Morphological Reconstruction, IEEE Trans. Med. Imag., 25 (2006), 1200-1213. doi: 10.1109/TMI.2006.879955.  Google Scholar

[30]

C. E. Metz, Basic principles of ROC analysis, Seminars Nucl. Med., 8 (1978), 283-298. doi: 10.1016/S0001-2998(78)80014-2.  Google Scholar

[31]

D. Mumford and J. Shah, Optimal approximation by piecewise smooth functions and associated variational problems, Commun. Pure Appl. Math., 42 (1989), 577-685. doi: 10.1002/cpa.3160420503.  Google Scholar

[32]

M. Niemeijer, J. Staal, B. Ginneken, M. Long and M. D. Abramoff, Comparative study of retinal vessel segmentation methods on a new publicly available database, Proc. SPIE Med. Imag., 5370 (2004), 648-656. doi: 10.1117/12.535349.  Google Scholar

[33]

S. Osher and J. A. Sethian, Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi Formulation, J. Comput. Phys., 79 (1988), 12-49. doi: 10.1016/0021-9991(88)90002-2.  Google Scholar

[34]

Q. Sheng, Recent trends in splitting, adaptive and hybrid numerical methods for differential equations, Neural, Parallel and Sci. Comput., 16 (2008), 283-301.  Google Scholar

[35]

C. Sinthanayothin, J. F. Boyce, T. H. Williamson, H. L. Cook, E. Mensah, S. Lal and D. Usher, Automated detection of diabetic retinopathy on digital fundus images, Diabetic Med., 19 (2002), 105-112. doi: 10.1046/j.1464-5491.2002.00613.x.  Google Scholar

[36]

J. Soares, J. Leandro, J. Cesar, H. Jelinek and M. Cree, Retinal vessel segmentation using the 2-d gabor wavelet and supervised classification, IEEE Trans. Med. Imag., 25 (2006), 1214-1222. doi: 10.1109/TMI.2006.879967.  Google Scholar

[37]

J. Staal, M. Abramoff, M. Viergever and B. Ginneken, Ridge based vessel segmentation in color images of the retina, IEEE Trans. Med. Imag., 23 (2004), 501-509. doi: 10.1109/TMI.2004.825627.  Google Scholar

[38]

K. Sum and P. Cheung, Vessel extraction under non-uniform illumination: A level set approach, IEEE Trans. Biomed. Eng., 55 (2008), 358-360. doi: 10.1109/TBME.2007.896587.  Google Scholar

[39]

X. Tai, O. Christiansen, P. Lin and I. Skjaelaaen, A remark on the MBO scheme and some piecewise constant level set methods, Int. J. Comput. Vis., 73 (2007), 61-76. Google Scholar

[40]

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, 2199 (2001), 282-287. ser. Lecture Notes in Computer Science. doi: 10.1007/3-540-45497-7_43.  Google Scholar

[41]

L. Wang, A. Bhalerao and R. Wilson, Analysis of retinal vasculature using a multiresolution hermite model, IEEE Trans. Med. Imag., 26 (2007), 137-152. doi: 10.1109/TMI.2006.889732.  Google Scholar

[42]

Y. Wang, G. Ji, P. Lin and E. Trucco, Retinal vessel segmentation using matched filter with multiwavelet kernels and multiscale hierachical decomposition, Pattern Recog., 46 (2013), 2117-2133. Google Scholar

[43]

C. Wu and X. Tai, Augmented Lagrangian method, Dual methods and Split-Bregman Iterations for ROF, vectorial TV and higher order models, SIAM J. Imag. Sci., 3 (2010), 300-339. doi: 10.1137/090767558.  Google Scholar

[44]

F. Zana and J. C. Klein, Segmentation of vessel-like patterns using mathematical morphology and curvature evaluation, IEEE Trans. Imag. Proc., 10 (2001), 1010-1019. doi: 10.1109/83.931095.  Google Scholar

[45]

D. Zonoobi, A. Kassim and W. Shen, Vasculature segmentation in MRA images using gradient compensated geodesic active contours, J. Sign. Process. Syst., 54 (2009), 171-181. doi: 10.1007/s11265-008-0216-4.  Google Scholar

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