• Previous Article
    Melnikov analysis of the nonlocal nanobeam resting on fractional-order softening nonlinear viscoelastic foundations
  • DCDS-S Home
  • This Issue
  • Next Article
    The surface diffusion and the Willmore flow for uniformly regular hypersurfaces
doi: 10.3934/dcdss.2020386

Signed-distance function based non-rigid registration of image series with varying image intensity

a. 

Department of Mathematics, FNSPE, Czech Technical University in Prague, Trojanova 13,120 00 Prague, Czech Republic

b. 

Department of Radiology, Institute for clinical and experimental medicine, Vídeňská 1958/9, Praha 4,140 21, Czech Republic

c. 

Inria, France

d. 

LMS, Ecole Polytechnique, CNRS, Institut Polytechnique de Paris, France

e. 

School of Biomedical Engineering & Imaging Sciences, St Thomas' Hospital, King's College London, UK

* Corresponding author: katerina.skardova@fjfi.cvut.cz

Received  January 2019 Revised  February 2020 Published  June 2020

In this paper we propose a method for locally adjusted optical flow-based registration of multimodal images, which uses the segmentation of object of interest and its representation by the signed-distance function (OF$ ^{dist} $ method). We deal with non-rigid registration of the image series acquired by the Modiffied Look-Locker Inversion Recovery (MOLLI) magnetic resonance imaging sequence, which is used for a pixel-wise estimation of $ T_1 $ relaxation time. The spatial registration of the images within the series is necessary to compensate the patient's imperfect breath-holding. The evolution of intensities and a large variation of image contrast within the MOLLI image series, together with the myocardium of left ventricle (the object of interest) typically not being the most distinct object in the scene, makes the registration challenging. The paper describes all components of the proposed OF$ ^{dist} $ method and their implementation. The method is then compared to the performance of a standard mutual information maximization-based registration method, applied either to the original image (MIM) or to the signed-distance function (MIM$ ^{dist} $). Several experiments with synthetic and real MOLLI images are carried out. On synthetic image with a single object, MIM performed the best, while OF$ ^{dist} $ and MIM$ ^{dist} $ provided better results on synthetic images with more than one object and on real images. When applied to signed-distance function of two objects of interest, MIM$ ^{dist} $ provided a larger registration error, but more homogeneously distributed, compared to OF$ ^{dist} $. For the real MOLLI image series with left ventricle pre-segmented using a level-set method, the proposed OF$ ^{dist} $ registration performed the best, as is demonstrated visually and by measuring the increase of mutual information in the object of interest and its neighborhood.

Citation: Kateřina Škardová, Tomáš Oberhuber, Jaroslav Tintěra, Radomír Chabiniok. Signed-distance function based non-rigid registration of image series with varying image intensity. Discrete & Continuous Dynamical Systems - S, doi: 10.3934/dcdss.2020386
References:
[1]

J. F. Aujol and G. Aubert, Signed distance functions and viscosity solutions of discontinuous Hamilton-Jacobi Equations, INRIA Res. Rep, 4507 (2002).   Google Scholar

[2]

V. CasellesR. Kimmel and G. Sapiro, Geodesic active contours, Proceedings of Fifth International Conference on Computer Vision, (1995), 694-699.   Google Scholar

[3]

S. Chen and A. Rahman, Contrast enhancement using recursive mean-separate histogram equalisation for scalable brightness preservation, IEEE Transactions on Consumer Electronics, 49 (2003), 1301-1309.   Google Scholar

[4]

L. C. Evans and J. Spruck, Motion of level sets by mean curvature. Ⅰ, Journal of Differential Geometry, 33 (1991), 635-681.  doi: 10.4310/jdg/1214446559.  Google Scholar

[5]

M. A. Fischler and R. A. Elschlager, The representation and matching of pictorial structures, IEEE Transactions on computers, C-22 (1973), 67-92.  doi: 10.1109/T-C.1973.223602.  Google Scholar

[6]

I. M. Gelfand, R. A. Silverman and et al., Calculus of Variations, Courier Corporation, 2000. Google Scholar

[7]

A. HandlovičováK. Mikula and F. Sgallari, Semi-implicit complementary volume scheme for solving level set like equations in image processing and curve evolution, Numerische Mathematik, 93 (2003), 675-695.  doi: 10.1007/s002110100374.  Google Scholar

[8]

B. K. P. Horn and B. G. Schunck, Determining optical flow, Artificial Intelligence, 17 (1981), 185-203.  doi: 10.1016/0004-3702(81)90024-2.  Google Scholar

[9]

J. Jost, Postmodern Analysis, Third edition, Universitext, Springer-Verlag, Berlin, 2005.  Google Scholar

[10]

V. KlementT. Oberhuber and D. Ševčovič, Application of the level-set model with constraints in image segmentation, Numerical Mathematics: Theory, Methods and Applications, 9 (2016), 147-168.  doi: 10.4208/nmtma.2015.m1418.  Google Scholar

[11]

F. MaesA. CollignonD. VandermeulenG. Marchal and P. Suetens, Multimodality image registration by maximisation of mutual information, IEEE Transactions on Medical Imaging, 16 (1997), 187-198.   Google Scholar

[12]

J. A. Maintz and M. A. Viergever, A survey of medical image registration, Medical Image Analysis, 2 (1998), 1-36.  doi: 10.1016/S1361-8415(01)80026-8.  Google Scholar

[13]

T. MakelaP. ClarysseO. SipilaN. PaunaQ. C. PhamT. Katila and I. E. Magnin, A review of cardiac image registration methods, IEEE Transactions on Medical Imaging, 21 (2002), 1011-1021.  doi: 10.1109/TMI.2002.804441.  Google Scholar

[14]

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

[15]

D. P. PengB. MerrimanS. OsherH. K. Zhao and M. Kang, A PDE-based fast local level set method, Journal of Computational Physics, 155 (1999), 410-438.  doi: 10.1006/jcph.1999.6345.  Google Scholar

[16]

P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (1990), 629-639.  doi: 10.1109/34.56205.  Google Scholar

[17]

G. Peyré, M. Péchaud, R. Keriven, L. D. Cohen and et al., Geodesic methods in computer vision and graphics, Foundations and Trends® in Computer Graphics and Vision, 5 (2010), 197–397. Google Scholar

[18]

R. Precup, Methods in Nonlinear Integral Equations, Kluwer Academic Publishers, Dordrecht, 2002. doi: 10.1007/978-94-015-9986-3.  Google Scholar

[19]

D. RueckertL. I. SonodaC. HayesD. L. HillM. O. Leach and D. J. Hawkes, Nonrigid registration using free-form deformations: Application to breast MR images, IEEE Transactions on Medical Imaging, 18 (1999), 712-721.  doi: 10.1109/42.796284.  Google Scholar

[20]

J. A. Sethian, Numerical algorithms for propagating interfaces: Hamilton-Jacobi equations and conservation laws, Journal of Differential Geometry, 31 (1990), 131-161.  doi: 10.4310/jdg/1214444092.  Google Scholar

[21]

J. A. Sethian, Level Set Methods: Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision, and Materials Science, Cambridge Monographs on Applied and Computational Mathematics, 3. Cambridge University Press, Cambridge, 1996.  Google Scholar

[22]

T. W. Tang and A. C. Chung, Non-rigid image registration using graph-cuts, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, (2007), 916-924.   Google Scholar

show all references

References:
[1]

J. F. Aujol and G. Aubert, Signed distance functions and viscosity solutions of discontinuous Hamilton-Jacobi Equations, INRIA Res. Rep, 4507 (2002).   Google Scholar

[2]

V. CasellesR. Kimmel and G. Sapiro, Geodesic active contours, Proceedings of Fifth International Conference on Computer Vision, (1995), 694-699.   Google Scholar

[3]

S. Chen and A. Rahman, Contrast enhancement using recursive mean-separate histogram equalisation for scalable brightness preservation, IEEE Transactions on Consumer Electronics, 49 (2003), 1301-1309.   Google Scholar

[4]

L. C. Evans and J. Spruck, Motion of level sets by mean curvature. Ⅰ, Journal of Differential Geometry, 33 (1991), 635-681.  doi: 10.4310/jdg/1214446559.  Google Scholar

[5]

M. A. Fischler and R. A. Elschlager, The representation and matching of pictorial structures, IEEE Transactions on computers, C-22 (1973), 67-92.  doi: 10.1109/T-C.1973.223602.  Google Scholar

[6]

I. M. Gelfand, R. A. Silverman and et al., Calculus of Variations, Courier Corporation, 2000. Google Scholar

[7]

A. HandlovičováK. Mikula and F. Sgallari, Semi-implicit complementary volume scheme for solving level set like equations in image processing and curve evolution, Numerische Mathematik, 93 (2003), 675-695.  doi: 10.1007/s002110100374.  Google Scholar

[8]

B. K. P. Horn and B. G. Schunck, Determining optical flow, Artificial Intelligence, 17 (1981), 185-203.  doi: 10.1016/0004-3702(81)90024-2.  Google Scholar

[9]

J. Jost, Postmodern Analysis, Third edition, Universitext, Springer-Verlag, Berlin, 2005.  Google Scholar

[10]

V. KlementT. Oberhuber and D. Ševčovič, Application of the level-set model with constraints in image segmentation, Numerical Mathematics: Theory, Methods and Applications, 9 (2016), 147-168.  doi: 10.4208/nmtma.2015.m1418.  Google Scholar

[11]

F. MaesA. CollignonD. VandermeulenG. Marchal and P. Suetens, Multimodality image registration by maximisation of mutual information, IEEE Transactions on Medical Imaging, 16 (1997), 187-198.   Google Scholar

[12]

J. A. Maintz and M. A. Viergever, A survey of medical image registration, Medical Image Analysis, 2 (1998), 1-36.  doi: 10.1016/S1361-8415(01)80026-8.  Google Scholar

[13]

T. MakelaP. ClarysseO. SipilaN. PaunaQ. C. PhamT. Katila and I. E. Magnin, A review of cardiac image registration methods, IEEE Transactions on Medical Imaging, 21 (2002), 1011-1021.  doi: 10.1109/TMI.2002.804441.  Google Scholar

[14]

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

[15]

D. P. PengB. MerrimanS. OsherH. K. Zhao and M. Kang, A PDE-based fast local level set method, Journal of Computational Physics, 155 (1999), 410-438.  doi: 10.1006/jcph.1999.6345.  Google Scholar

[16]

P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, 12 (1990), 629-639.  doi: 10.1109/34.56205.  Google Scholar

[17]

G. Peyré, M. Péchaud, R. Keriven, L. D. Cohen and et al., Geodesic methods in computer vision and graphics, Foundations and Trends® in Computer Graphics and Vision, 5 (2010), 197–397. Google Scholar

[18]

R. Precup, Methods in Nonlinear Integral Equations, Kluwer Academic Publishers, Dordrecht, 2002. doi: 10.1007/978-94-015-9986-3.  Google Scholar

[19]

D. RueckertL. I. SonodaC. HayesD. L. HillM. O. Leach and D. J. Hawkes, Nonrigid registration using free-form deformations: Application to breast MR images, IEEE Transactions on Medical Imaging, 18 (1999), 712-721.  doi: 10.1109/42.796284.  Google Scholar

[20]

J. A. Sethian, Numerical algorithms for propagating interfaces: Hamilton-Jacobi equations and conservation laws, Journal of Differential Geometry, 31 (1990), 131-161.  doi: 10.4310/jdg/1214444092.  Google Scholar

[21]

J. A. Sethian, Level Set Methods: Evolving Interfaces in Geometry, Fluid Mechanics, Computer Vision, and Materials Science, Cambridge Monographs on Applied and Computational Mathematics, 3. Cambridge University Press, Cambridge, 1996.  Google Scholar

[22]

T. W. Tang and A. C. Chung, Non-rigid image registration using graph-cuts, International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Springer, (2007), 916-924.   Google Scholar

Figure 1.  Diagram of the proposed method
Figure 2.  Volume elements and the central points
Figure 3.  Source image (A), target image (B), the absolute value of their difference before registration (C) and after registration by OF$ ^{dist} $, MIM and MIM$ ^{dist} $ (D-F). Parameters of OF$ ^{dist} $: $ N_1 = 200, N_2 = 200, \alpha = 1.0, \beta = 3.25, \gamma = 2.5 $
Figure 4.  Source image (A), target image (B) and the absolute value of their difference before (C) and after registration by OF, MIM and MIM$ ^{dist} $ (D-F). Parameters of OF$ ^{dist} $: $ N_1 = 200, N_2 = 200, \alpha = 1.5, \beta = 3.75, \gamma = 3.25 $
Figure 5.  Absolute value of the difference between the target and source signed-distance function before and after registration by OF$ ^{dist} $ and MIM$ ^{dist} $. The signed-distance functions are computed on 10-pixel-wide neighborhood of the edges of the object and set to constant outside the neighborhood
Figure 6.  The results of OF$ ^{dist} $ registration of object marked by green line in 6a, and global MIM registration of the whole scene
Figure 7.  Images from the MOLLI sequence with segmented myocardium. Parameters for the outer edge detection: $ K_{S_1} = 1.3\cdot 10^{-6} $, $ K_{S_2} = 1.3\cdot 10^{-6} $, $ K_{T} = 2.3\cdot 10^{-6} $. Parameters for the inner edge detection: $ K_{S_1} = K_{S_2} = K_{T} = 9.0\cdot 10^{-6} $
Figure 8.  Results of registration of $ S_1,S_2 $ with target image $ T $. Parameters of OF$ ^{dist} $: $ N_1 = 256, N_2 = 218, \alpha = 1.25, \beta = 3.5, \gamma = 3.0 $
Table 1.  Norms of difference between the target 3b image and source image 3a before and after registration by OF$ ^{dist} $, MIM and MIM$ ^{dist} $
$ \| T - S \|_2 $ $ \| T - S_{OF^{dist}} \|_2 $ $ \| T - S_{MIM} \|_2 $ $ \| T - S_{MIM^{dist}} \|_2 $
9462.47 3130.83 2173.93 2607.66
$ \| T - S \|_2 $ $ \| T - S_{OF^{dist}} \|_2 $ $ \| T - S_{MIM} \|_2 $ $ \| T - S_{MIM^{dist}} \|_2 $
9462.47 3130.83 2173.93 2607.66
Table 2.  Norms of difference between the target image in Figure 4b image and source image in Figure 4a before and after registration by OF$ ^{dist} $, MIM and MIM$ ^{dist} $
$ \| T - S \|_2 $ $ \| T - S_{OF^{dist}} \|_2 $ $ \| T - S_{MIM} \|_2 $ $ \| T - S_{MIM^{dist}} \|_2 $
6654.44 2199.07 4121.95 3602.27
$ \| T - S \|_2 $ $ \| T - S_{OF^{dist}} \|_2 $ $ \| T - S_{MIM} \|_2 $ $ \| T - S_{MIM^{dist}} \|_2 $
6654.44 2199.07 4121.95 3602.27
Table 3.  Norms of difference between target and source signed-distance function before and after registration by OF$ ^{dist} $ and MIM$ ^{dist} $. The source and target objects can be seen in Figure 4a and 4b, respectively
$ \| \phi_T - \phi_S \|_2 $ $ \| \phi_T - \phi_{S,{OF^{dist}}} \|_2 $ $ \| \phi_T - \phi_{S,{MIM^{dist}}} \|_2 $
0.849915 0.239367 0.2950385
$ \| \phi_T - \phi_S \|_2 $ $ \| \phi_T - \phi_{S,{OF^{dist}}} \|_2 $ $ \| \phi_T - \phi_{S,{MIM^{dist}}} \|_2 $
0.849915 0.239367 0.2950385
Table 4.  Norms of difference between the target image 4b and source image 4a before registration, after registration of one object by OF$ ^{dist} $, and after global registration by MIM
$ \| T - S \|_2 $ $ \| T - S_{1,OF^{dist}} \|_2 $ $ \| T - S_{MIM} \|_2 $
6654.44 5062.1 4121.95
$ \| T - S \|_2 $ $ \| T - S_{1,OF^{dist}} \|_2 $ $ \| T - S_{MIM} \|_2 $
6654.44 5062.1 4121.95
Table 5.  MI target image and source images from Figure 8 before and after registration by OF$ ^{dist} $ and MIM and MIM$ ^{dist} $. The MI was computed only in the surroundings of the segmented objects
$ i $ $ MI(T,S_i) $ $ MI(T,S_{i,OF^{dist}}) $ $ MI(T,S_{i,MIM}) $ $ MI(T,S_{i,MIM^{dist}}) $
1 1.1556 1.2184 1.2273 1.2170
2 1.1012 1.2034 1.2052 1.1964
$ i $ $ MI(T,S_i) $ $ MI(T,S_{i,OF^{dist}}) $ $ MI(T,S_{i,MIM}) $ $ MI(T,S_{i,MIM^{dist}}) $
1 1.1556 1.2184 1.2273 1.2170
2 1.1012 1.2034 1.2052 1.1964
[1]

Dana Paquin, Doron Levy, Eduard Schreibmann, Lei Xing. Multiscale Image Registration. Mathematical Biosciences & Engineering, 2006, 3 (2) : 389-418. doi: 10.3934/mbe.2006.3.389

[2]

Zhao Yi, Justin W. L. Wan. An inviscid model for nonrigid image registration. Inverse Problems & Imaging, 2011, 5 (1) : 263-284. doi: 10.3934/ipi.2011.5.263

[3]

Fan Jia, Xue-Cheng Tai, Jun Liu. Nonlocal regularized CNN for image segmentation. Inverse Problems & Imaging, 2020, 14 (5) : 891-911. doi: 10.3934/ipi.2020041

[4]

Dana Paquin, Doron Levy, Lei Xing. Hybrid multiscale landmark and deformable image registration. Mathematical Biosciences & Engineering, 2007, 4 (4) : 711-737. doi: 10.3934/mbe.2007.4.711

[5]

Christiane Pöschl, Jan Modersitzki, Otmar Scherzer. A variational setting for volume constrained image registration. Inverse Problems & Imaging, 2010, 4 (3) : 505-522. doi: 10.3934/ipi.2010.4.505

[6]

Dominique Zosso, Jing An, James Stevick, Nicholas Takaki, Morgan Weiss, Liane S. Slaughter, Huan H. Cao, Paul S. Weiss, Andrea L. Bertozzi. Image segmentation with dynamic artifacts detection and bias correction. Inverse Problems & Imaging, 2017, 11 (3) : 577-600. doi: 10.3934/ipi.2017027

[7]

Matthew S. Keegan, Berta Sandberg, Tony F. Chan. A multiphase logic framework for multichannel image segmentation. Inverse Problems & Imaging, 2012, 6 (1) : 95-110. doi: 10.3934/ipi.2012.6.95

[8]

Ye Yuan, Yan Ren, Xiaodong Liu, Jing Wang. Approach to image segmentation based on interval neutrosophic set. Numerical Algebra, Control & Optimization, 2020, 10 (1) : 1-11. doi: 10.3934/naco.2019028

[9]

Marcus Wagner. A direct method for the solution of an optimal control problem arising from image registration. Numerical Algebra, Control & Optimization, 2012, 2 (3) : 487-510. doi: 10.3934/naco.2012.2.487

[10]

Angel Angelov, Marcus Wagner. Multimodal image registration by elastic matching of edge sketches via optimal control. Journal of Industrial & Management Optimization, 2014, 10 (2) : 567-590. doi: 10.3934/jimo.2014.10.567

[11]

Yangang Chen, Justin W. L. Wan. Numerical method for image registration model based on optimal mass transport. Inverse Problems & Imaging, 2018, 12 (2) : 401-432. doi: 10.3934/ipi.2018018

[12]

Jianping Zhang, Ke Chen, Bo Yu, Derek A. Gould. A local information based variational model for selective image segmentation. Inverse Problems & Imaging, 2014, 8 (1) : 293-320. doi: 10.3934/ipi.2014.8.293

[13]

Ruiliang Zhang, Xavier Bresson, Tony F. Chan, Xue-Cheng Tai. Four color theorem and convex relaxation for image segmentation with any number of regions. Inverse Problems & Imaging, 2013, 7 (3) : 1099-1113. doi: 10.3934/ipi.2013.7.1099

[14]

Jie Huang, Xiaoping Yang, Yunmei Chen. A fast algorithm for global minimization of maximum likelihood based on ultrasound image segmentation. Inverse Problems & Imaging, 2011, 5 (3) : 645-657. doi: 10.3934/ipi.2011.5.645

[15]

Shi Yan, Jun Liu, Haiyang Huang, Xue-Cheng Tai. A dual EM algorithm for TV regularized Gaussian mixture model in image segmentation. Inverse Problems & Imaging, 2019, 13 (3) : 653-677. doi: 10.3934/ipi.2019030

[16]

Lu Tan, Ling Li, Senjian An, Zhenkuan Pan. Nonlinear diffusion based image segmentation using two fast algorithms. Mathematical Foundations of Computing, 2019, 2 (2) : 149-168. doi: 10.3934/mfc.2019011

[17]

Balázs Kósa, Karol Mikula, Markjoe Olunna Uba, Antonia Weberling, Neophytos Christodoulou, Magdalena Zernicka-Goetz. 3D image segmentation supported by a point cloud. Discrete & Continuous Dynamical Systems - S, 2020  doi: 10.3934/dcdss.2020351

[18]

Yunmei Chen, Jiangli Shi, Murali Rao, Jin-Seop Lee. Deformable multi-modal image registration by maximizing Rényi's statistical dependence measure. Inverse Problems & Imaging, 2015, 9 (1) : 79-103. doi: 10.3934/ipi.2015.9.79

[19]

Mohamed Alahyane, Abdelilah Hakim, Amine Laghrib, Said Raghay. Fluid image registration using a finite volume scheme of the incompressible Navier Stokes equation. Inverse Problems & Imaging, 2018, 12 (5) : 1055-1081. doi: 10.3934/ipi.2018044

[20]

Huan Han. A variational model with fractional-order regularization term arising in registration of diffusion tensor image. Inverse Problems & Imaging, 2018, 12 (6) : 1263-1291. doi: 10.3934/ipi.2018053

2019 Impact Factor: 1.233

Metrics

  • PDF downloads (14)
  • HTML views (42)
  • Cited by (0)

[Back to Top]