Large Deformation Registration via n-dimensional Quasi-conformal Maps

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Abstract

We propose a new method to obtain registration between n-dimensional manifolds with very large deformations. Given a set of landmark correspondences, our algorithm produces an optimal diffeomorphism that matches prescribed landmark constraints. The obtained registration is a n-dimensional quasi-conformal map. The basic idea of the model is to minimize an energy functional with a conformality term and a smoothness term. The conformality term allows the algorithm to produce diffeomorphisms even with very large deformations. We minimize the energy functional using alternating direction method of multipliers (ADMM). The algorithm only involves solving an elliptic problem and a point-wise minimization problem. The time complexity and robustness of the algorithm is independent of the number of landmark constraints. Either Dirichlet or free boundary condition can be enforced, depending on applications. To further speed up the algorithm, the multi-grid method is applied. Experiments are carried out to test our proposed algorithm to compute landmark-matching registration with different landmark constraints. Results show that our proposed model is efficient to obtain a diffeomorphic registration between n-dimensional data with large deformations.

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Most cited references 27

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B.S. Reddy, B.N Chatterji (1996)

This correspondence discusses an extension of the well-known phase correlation technique to cover translation, rotation, and scaling. Fourier scaling properties and Fourier rotational properties are used to find scale and rotational movement. The phase correlation technique determines the translational movement. This method shows excellent robustness against random noise.

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G.E. Christensen, R.D. Rabbitt, M.I. Miller (1996)

A general automatic approach is presented for accommodating local shape variation when mapping a two-dimensional (2-D) or three-dimensional (3-D) template image into alignment with a topologically similar target image. Local shape variability is accommodated by applying a vector-field transformation to the underlying material coordinate system of the template while constraining the transformation to be smooth (globally positive definite Jacobian). Smoothness is guaranteed without specifically penalizing large-magnitude deformations of small subvolumes by constraining the transformation on the basis of a Stokesian limit of the fluid-dynamical Navier-Stokes equations. This differs fundamentally from quadratic penalty methods, such as those based on linearized elasticity or thin-plate splines, in that stress restraining the motion relaxes over time allowing large-magnitude deformations. Kinematic nonlinearities are inherently necessary to maintain continuity of structures during large-magnitude deformations, and are included in all results. After initial global registration, final mappings are obtained by numerically solving a set of nonlinear partial differential equations associated with the constrained optimization problem. Automatic regridding is performed by propagating templates as the nonlinear transformations evaluated on a finite lattice become singular. Application of the method to intersubject registration of neuroanatomical structures illustrates the ability to account for local anatomical variability.

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Landmark matching via large deformation diffeomorphisms.

S.C. Joshi, M.I. Miller (2000)

This paper describes the generation of large deformation diffeomorphisms phi:Omega=[0,1]3 Omega for landmark matching generated as solutions to the transport equation dphi(x,t)/dt=nu(phi(x,t),t),epsilon[0,1] and phi(x,0)=x, with the image map defined as phi(.,1) and therefore controlled via the velocity field nu(.,t),epsilon[0,1]. Imagery are assumed characterized via sets of landmarks {xn, yn, n=1, 2, ..., N}. The optimal diffeomorphic match is constructed to minimize a running smoothness cost parallelLnu parallel2 associated with a linear differential operator L on the velocity field generating the diffeomorphism while simultaneously minimizing the matching end point condition of the landmarks. Both inexact and exact landmark matching is studied here. Given noisy landmarks xn matched to yn measured with error covariances Sigman, then the matching problem is solved generating the optimal diffeomorphism phi;(x,1)=integral0(1)nu(phi(x,t),t)dt+x where nu(.)=argmin(nu.)integral1(0) integralOmega parallelLnu(x,t) parallel2dxdt +Sigman=1N[yn-phi(xn,1)] TSigman(-1)[yn-phi(xn,1)]. Conditions for the existence of solutions in the space of diffeomorphisms are established, with a gradient algorithm provided for generating the optimal flow solving the minimum problem. Results on matching two-dimensional (2-D) and three-dimensional (3-D) imagery are presented in the macaque monkey.