Abstract
In this paper, we address the issue of designing a theoretically well-motivated segmentation-guided registration method capable of handling large and smooth deformations. The shapes to be matched are viewed as hyperelastic materials and more precisely as Saint Venant–Kirchhoff ones and are implicitly modeled by level set functions. These are driven in order to minimize a functional containing both a nonlinear-elasticity-based regularizer prescribing the nature of the deformation, and a criterion that forces the evolving shape to match intermediate topology-preserving segmentation results. Theoretical results encompassing existence of minimizers, existence of a weak viscosity solution of the related evolution problem and asymptotic results are given. The study is then complemented by the derivation of the discrete counterparts of the asymptotic results provided in the continuous domain. Both a pure quadratic penalization method and an augmented Lagrangian technique (involving a related dual problem) are investigated with convergence results.
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References
Ambrosio, L., Dal Maso, G.: A general chain rule for distributional derivatives. Proc. Am. Math. Soc. 108(3), 691–702 (1990)
An, J.H., Chen, Y., Huang, F., Wilson, D., Geiser, E.: Medical Image Computing and Computer-Assisted Intervention—MICCAI 2005: 8th International Conference, Palm Springs, CA, USA, October 26–29, 2005. Proceedings, Part I, chap. A Variational PDE Based Level Set Method for a Simultaneous Segmentation and Non-Rigid Registration, pp. 286–293. Springer, Berlin (2005)
Ashburner, J., Friston, K.J.: Nonlinear spatial normalization using basis functions. Hum. Brain Mapp. 7(4), 254–266 (1999)
Aubert, G., Kornprobst, P.: Mathematical Problems in Image Processing: Partial Differential Equations and the Calculus of Variations. Applied Mathematical Sciences. Springer, New York (2001)
Barles, G., Cardaliaguet, P., Ley, O., Monteillet, A.: Existence of weak solutions for general nonlocal and nonlinear second-order parabolic equations. Nonlinear Anal. Theory Methods Appl. 71(7–8), 2801–2810 (2009)
Beg, M., Miller, M., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vis. 61(2), 139–157 (2005)
Bourgoing, M.: Viscosity solutions of fully nonlinear second order parabolic equations with \({L}^1\) dependence in time and Neumann boundary conditions. Discret. Contin. Dyn. Syst. 21(3), 763–800 (2008)
Brezis, H.: Analyse fonctionelle. Théorie et Applications. Dunod, Paris (2005)
Broit, C.: Optimal registration of Deformed Images. Ph.D. thesis, Computer and Information Science, University of Pennsylvania (1981)
Burachik, R.S., Gasimov, R.N., Ismayilova, N.A., Kaya, C.Y.: On a modified subgradient algorithm for dual problems via sharp augmented Lagrangian. J. Global Optim. 34(1), 55–78 (2005). doi:10.1007/s10898-005-3270-5
Burger, M., Modersitzki, J., Ruthotto, L.: A hyperelastic regularization energy for image registration. SIAM J. Sci. Comput. 35(1), B132–B148 (2013)
Caselles, V., Kimmel, R., Sapiro, G.: Geodesic active contours. Int. J. Comput. Vis. 22(1), 61–87 (1993)
Christensen, G., Rabbitt, R., Miller, M.: Deformable templates using large deformation kinematics. IEEE Trans. Image Process. 5(10), 1435–1447 (1996)
Christensen, G.E.: Deformable shape models for anatomy. Ph.D. thesis, Washington University, Sever Institute of technology, USA (1994)
Ciarlet, P.: Elasticité Tridimensionnelle. Masson, Paris (1985)
Ciarlet, P.: Mathematical Elasticity, Volume I: Three-Dimensional Elasticity. Amsterdam etc., North-Holland (1988)
Clatz, O., Sermesant, M., Bondiau, P.Y., Delingette, H., Warfield, S.K., Malandain, G., Ayache, N.: Realistic simulation of the 3-D growth of brain tumors in MR images coupling diffusion with biomechanical deformation. IEEE Trans. Med. Imaging 24(10), 1334–1346 (2005)
Crandall, M., Ishii, H., Lions, P.L.: User’s guide to viscosity solutions of second order partial differential equations. Bull. Am. Math. Soc. 27, 1–67 (1992)
Dacorogna, B.: Direct Methods in the Calculus of Variations, 2nd edn. Springer, New York (2008)
Davatzikos, C.: Spatial transformation and registration of brain images using elastically deformable models. Comput. Vis. Image Underst. 66(2), 207–222 (1997)
Davis, M.H., Khotanzad, A., Flamig, D.P., Harms, S.E.: A physics-based coordinate transformation for 3-D image matching. IEEE Trans. Med. Imaging 16(3), 317–328 (1997)
Demengel, F., Demengel, G., Erné, R.: Functional Spaces for the Theory of Elliptic Partial Differential Equations. Universitext. Springer, London (2012)
Derfoul, R., Le Guyader, C.: A relaxed problem of registration based on the Saint Venant–Kirchhoff material stored energy for the mapping of mouse brain gene expression data to a neuroanatomical mouse atlas. SIAM J. Imaging Sci. 7(4), 2175–2195 (2014)
Droske, M., Ring, W., Rumpf, M.: Mumford–Shah based registration: a comparison of a level set and a phase field approach. Comput. Vis. Sci. 12(3), 101–114 (2008)
Droske, M., Rumpf, M.: A variational approach to non-rigid morphological registration. SIAM J. Appl. Math. 64(2), 668–687 (2004)
Droske, M., Rumpf, M.: Multiscale joint segmentation and registration of image morphology. IEEE Trans. Pattern Anal. Mach. Intell. 29(12), 2181–2194 (2007)
Fischer, B., Modersitzki, J.: Fast diffusion registration. AMS Contemp. Math. Inverse Probl. Image Anal. Med. Imaging 313, 11–129 (2002)
Fischer, B., Modersitzki, J.: Curvature based image registration. J. Math. Imaging Vis. 18(1), 81–85 (2003)
Forcadel, N., Le Guyader, C.: A short time existence/uniqueness result for a nonlocal topology-preserving segmentation model. J. Differ. Equ. 253(3), 977–995 (2012)
Gasimov, R.N.: Augmented Lagrangian duality and nondifferentiable optimization methods in nonconvex programming. J. Global Optim. 24(2), 187–203 (2002). doi:10.1023/A:1020261001771
Gooya, A., Pohl, K., Bilello, M., Cirillo, L., Biros, G., Melhem, E., Davatzikos, C.: GLISTR: glioma image segmentation and registration. IEEE Trans. Med. Imaging 31(10), 1941–1954 (2012)
Gorthi, S., Duay, V., Bresson, X., Cuadra, M.B., Castro, F.J.S., Pollo, C., Allal, A.S., Thiran, J.P.: Active deformation fields: dense deformation field estimation for atlas-based segmentation using the active contour framework. Med. Image Anal. 15(6), 787–800 (2011)
Haber, E., Heldmann, S., Modersitzki, J.: A computational framework for image-based constrained registration. Linear Algebra Its Appl. 431(3–4), 459–470 (2009). (Special Issue in honor of Henk van der Vorst)
Haber, E., Modersitzki, J.: Numerical methods for volume preserving image registration. Inverse Probl. 20(5), 1621–1638 (2004)
Haber, E., Modersitzki, J.: Image registration method with guaranteed displacement regularity. Int. J. Comput. Vis. 71(3), 361–372 (2007)
Karaçali, B., Davatzikos, C.: Estimating topology preserving and smooth displacement fields. IEEE Trans. Med. Imaging 23(7), 868–880 (2004)
Le Dret, H., Raoult, A.: The quasi-convex envelope of the Saint Venant–Kirchhoff stored energy function. Proc. R. Soc. Edinb. Sect. A Math. 125(6), 1179–1192 (1995)
Le Guyader, C., Vese, L.: Self-repelling snakes for topology-preserving segmentation models. IEEE Trans. Image Process. 17(5), 767–779 (2008)
Le Guyader, C., Vese, L.: A combined segmentation and registration framework with a nonlinear elasticity smoother. Comput. Vis. Image Underst. 115(12), 1689–1709 (2011)
Lin, T., Le Guyader, C., Dinov, I., Thompson, P., Toga, A., Vese, L.: Gene expression data to mouse atlas registration using a nonlinear elasticity smoother and landmark points constraints. J. Sci. Comput. 50, 586–609 (2012)
Lord, N., Ho, J., Vemuri, B., Eisenschenk, S.: Simultaneous registration and parcellation of bilateral hippocampal surface pairs for local asymmetry quantification. IEEE Trans. Med. Imaging 26(4), 471–478 (2007)
Modersitzki, J.: Numerical Methods for Image Registration. Oxford University Press, Oxford (2004)
Musse, O., Heitz, F., Armspach, J.P.: Topology preserving deformable image matching using constrained hierarchical parametric models. IEEE Trans. Image Process. 10(7), 1081–1093 (2001)
Negrón Marrero, P.: A numerical method for detecting singular minimizers of multidimensional problems in nonlinear elasticity. Numer. Math. 58, 135–144 (1990)
Noblet, V., Heinrich, C., Heitz, F., Armspach, J.P.: 3-D deformable image registration: a topology preservation scheme based on hierarchical deformation models and interval analysis optimization. IEEE Trans. Image Process. 14(5), 553–566 (2005)
Ozeré, S., Gout, C., Le Guyader, C.: Joint segmentation/registration model by shape alignment via weighted total variation minimization and nonlinear elasticity. SIAM J. Imaging Sci. 8(3), 1981–2020 (2015)
Ozeré, S., Le Guyader, C.: Scale Space and Variational Methods in Computer Vision: 5th International Conference, SSVM 2015, Lège-Cap Ferret, France, May 31–June 4, 2015, Proceedings, chap. Nonlocal Joint Segmentation Registration Model, pp. 348–359. Springer International Publishing, Cham (2015)
Ozeré, S., Le Guyader, C.: Topology preservation for image-registration-related deformation fields. Commun. Math. Sci. 13(5), 1135–1161 (2015)
Rabbitt, R., Weiss, J., Christensen, G., Miller, M.: Mapping of hyperelastic deformable templates using the finite element method. In: Proceedings SPIE, vol. 2573, pp. 252–265. SPIE (1995)
Rockafellar, R.T.: Lagrange multipliers and optimiality. SIAM 35, 183–238 (1993)
Rumpf, M., Wirth, B.: A nonlinear elastic shape averaging approach. SIAM J. Imaging Sci. 2(3), 800–833 (2009)
Sederberg, T., Parry, S.: Free-form deformation of solid geometric models. SIGGRAPH Comput. Graph. 20(4), 151–160 (1986)
Sotiras, A., Davatzikos, C., Paragios, N.: Deformable medical image registration: a survey. IEEE Trans. Med. Imaging 32(7), 1153–1190 (2013)
Vemuri, B., Ye, J., Chen, Y., Leonard, C.: Image Registration via level-set motion: applications to atlas-based segmentation. Med. Image Anal. 7(1), 1–20 (2003)
Vese, L., Le Guyader, C.: Variational Methods in Image Processing. Chapman & Hall/CRC Mathematical and Computational Imaging Sciences Series. Taylor & Francis (2015)
Weickert, J., Kühne, G.: Geometric Level Set Methods in Imaging, Vision, and Graphics, chap. Fast Methods for Implicit Active Contour Models, pp. 43–57. Springer, New York (2003)
Wing-Sum, C.: Some discrete Poincaré-type inequalities. Int. J. Math. Math. Sci. 25(7), 479–488 (2001)
Yanovsky, I., Thompson, P.M., Osher, S., Leow, A.D.: Topology preserving log-unbiased nonlinear image registration: Theory and implementation. In: Proceedings IEEE Conference Computer Vision Pattern Recognition, pp. 1–8 (2007)
Yezzi, A., Zollei, L., Kapur, T.: A variational framework for joint segmentation and registration. In: Mathematical Methods in Biomedical Image Analysis, pp. 44–51. IEEE-MMBIA (2001)
Zagorchev, L., Goshtasby, A.: A comparative study of transformation functions for nonrigid image registration. IEEE Trans. Image Process. 15(3), 529–538 (2006)
Acknowledgements
The project is co-financed by the European Union with the European regional development fund (ERDF, HN0002137) and by the Normandie Regional Council via the M2NUM project. The authors would like to thank Dr. Caroline Petitjean (LITIS, Université de Rouen, France) for providing the cardiac cycle MRI images.
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Appendices
Appendix 1: Alternative Proof of Proposition 2
Proof
First, by definition (see [19, Chapitre 9, p. 432]), for almost every \(x\in \varOmega \) and for every \((\varphi ,\xi )\in {\mathbb {R}}^2 \times {\mathbb {R}}^4\), the quasi-convex envelope of f with respect to the last variable, denoted by Qf, is defined by:
\(D \subset {\mathbb {R}}^2\) being a bounded open set. Consequently, in our case, \(Qf(x,\varphi ,\xi )=\frac{\nu }{2}\,(H_{\varepsilon }(\varPhi _0 \circ \varphi )-H_{\varepsilon }({\tilde{\varPhi }}(\cdot ,{\bar{T}})))^2+QW(\xi )\).
We now recall a few useful definitions.
Definition 2
(Dacorogna [19, Notation 5.30, Definition 13.1])
-
The set of orthogonal matrices is denoted by O(n). It is the set of matrices \(R\in M_n({\mathbb {R}})\) (set of real square matrices of order n) such that
$$\begin{aligned} RR^T=I. \end{aligned}$$ -
The set of special orthogonal matrices, denoted by SO(n), is the subset of O(n) such that the matrices satisfy
$$\begin{aligned} \det R=1. \end{aligned}$$ -
Let \(\xi \in M_n({\mathbb {R}})\). The singular values of \(\xi \), denoted by
$$\begin{aligned} 0\le \lambda _1\le \dots \le \lambda _n, \end{aligned}$$are defined to be the square roots of the eigenvalues of the symmetric positive semi-definite matrix \(\xi \xi ^T \in M_n({\mathbb {R}})\).
-
The signed singular values of \(\xi \), denoted by
$$\begin{aligned} 0\le |\mu _1(\xi )|\le \mu _2(\xi )\le \dots \le \mu _n(\xi ), \end{aligned}$$are defined by
$$\begin{aligned}&\mu _1(\xi )=\lambda _1(\xi ){\text{ sign }}(\det \xi ) \, \text { and } \, \mu _j(\xi )=\lambda _j(\xi ) ,\\&\forall j=2,\dots , n. \end{aligned}$$ -
A function \(f:M_n({\mathbb {R}}) \rightarrow [-\infty , +\infty ]\) is said to be \(SO(n)\times SO(n)\) invariant if:
$$\begin{aligned}&\forall \xi \in M_n({\mathbb {R}}), \forall Q,R \in SO(n)\times SO(n),\nonumber \\&\quad f(Q\xi R) =f(\xi ). \end{aligned}$$
According to the singular value theorem ([19, Theorem 13.3]), for all \(\xi \in M_n({\mathbb {R}})\), we can find Q and R in SO(n) such that \(\xi =Q\varLambda R\), where \(\varLambda ={\text{ diag }}\,({\text{ sign }}(\det \xi )\lambda _1,\) \(\lambda _2,\dots , \lambda _n)\). In terms of singular values, function W can be rewritten by
Let
In view of the general results, we have \(PW\le QW\le RW \le W\), PW denoting the polyconvex envelope of W, QW, its quasi-convex envelope and RW, its rank-1 convex envelope (see [19, Chapter 6]). We are going to prove that
The stored energy function \(W(\xi )\) is \(SO(2)\times SO(2)\) invariant as a function of the trace and the determinant. Indeed,
Hence, according to [19, Theorem 6.20], PW, QW and RW are \(SO(2)\times SO(2)\) invariant. Therefore, we can restrict ourselves to the case of matrices of the form:
Then, we have \(\det \xi = x y\) and \( \Vert {\xi }\Vert ^2 =x^2+y^2\).
Before proceeding further, it is convenient to introduce the two following functions defined on \({\mathbb {R}}^2\) by
with \([z]^2_+ = \left\{ \begin{aligned}&z^2\quad \text { if } z\ge 0 \\&0 \quad \,\,\text { otherwise.} \end{aligned}\right. \)
A simple calculation leads to \( W\begin{pmatrix} x &{} 0 \\ 0 &{} y \end{pmatrix} =\chi (x,y)\) and \(g\begin{pmatrix} x &{} 0 \\ 0 &{} y \end{pmatrix}=\phi (x,y)\). According to [19, Definition 5.42], \(\phi \) is polyconvex and thus g is polyconvex ([19, Theorem 5.43]). Since \(g(\xi )\le W(\xi )\) and \(PW=\sup \{h\le W,\, h\,\, \text {polyconvex}\}\), we have
This result is not surprising since if one sets \(\chi (\xi )=\beta (\Vert \xi \Vert ^2-\alpha )^2\), it is well-known that
\(C\chi (\xi )=P\chi (\xi )=Q\chi (\xi )=\left\{ \begin{array}{ll} \beta (\Vert \xi \Vert ^2-\alpha )^2 &{} \text {if } \Vert \xi \Vert ^2\ge \alpha \\ 0 &{} \text {otherwise} \end{array}\right. \), C denoting the convex envelope, and according to the definition of the polyconvex envelope, it is clear that \(g(\xi )=\psi (\det \xi )+C\chi (\xi ) \le PW(\xi )\).
Case 1: \(\Vert \xi \Vert ^2 < \alpha \).
Let us set
Then
This implies that
Case 2: \(\Vert {\xi }\Vert ^2\ge \alpha \).
which concludes the proof. \(\square \)
Remark 9
In fact, we have proved a stronger result, namely, that the polyconvex envelope of W, PW, equals both the quasi-convex envelope of W, QW and the rank-1 convex envelope of W, RW.
Remark 10
We understand better through this proof the choice of the weighting parameter balancing the component \(\left( \det {\xi }-1\right) ^2\); it has been chosen in order that the mapping \(\varPsi \) is convex.
Appendix 2: Discrete Generalized Poincaré Inequality
We first recall the continuous generalized Poincaré inequality.
Theorem 12
(extracted from [22, p.106]) Let \(\varOmega \) be a bounded Lipschitz domain in \({\mathbb {R}}^N\). Let \(p \in [1;+\infty [\) and let \({\mathcal {N}}\) be a continuous seminorm on \(W^{1,p}(\varOmega )\), that is a norm on the constant functions. Suppose that \(u \in W^{1,p}(\varOmega )\), then there exists a constant \(C>0\) depending only on \(N, p, \varOmega \) such that:
We apply this result with \(\mathcal {N}(u)=\int _{\varGamma _0}\mid u(x) \mid dx\) when \(\varOmega \) is a \(C^1\) open set and \(\varGamma _0\) is a subset of \(\partial \varOmega \) with positive (N-1)-dimensional Lebesgue measure.
Now, we will provide a similar discrete inequality. The following result is an adaption of the one from [57] given for real-valued functions which vanish on the boundary, whereas the results presented here stand for \({\mathbb {R}}^2\)-valued functions which are equal to the identity on the boundary.
Theorem 13
(adapted from [57, Corollary 3.10]) (Discrete generalized Poincaré inequality). For any \(f \in \mathcal {F}_1(\tilde{\varOmega })\), and any real number \(q \ge 2\), we have:
with \(c_4=B^{q} 2^{\frac{7q+2}{2}} \left[ 8NM+ \frac{4}{N} \frac{M(M+1)(2M+1)}{6} \right. \) \(\left. + \frac{4}{M} \frac{N(N+1)(2N+1)}{6} \right] ^{\frac{q}{2}}\) with \(B=\max \{M,N\}\).
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Debroux, N., Ozeré, S. & Le Guyader, C. A Non-local Topology-Preserving Segmentation-Guided Registration Model. J Math Imaging Vis 59, 432–455 (2017). https://doi.org/10.1007/s10851-016-0699-8
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DOI: https://doi.org/10.1007/s10851-016-0699-8