TY - JOUR AU - Akinlar, Mehmet Ali AU - Kurulay, Muhammet AU - Secer, Aydin AU - Celenk, Mehmet PY - 2012 DA - 2012/11/07 TI - Curvature-driven diffusion-based mathematical image registration models JO - Advances in Difference Equations SP - 193 VL - 2012 IS - 1 AB - This paper introduces several mathematical image registration models. Image registration, an ill-posed optimization problem, is formulated as the minimization of the sum of an image similarity metric and a regularization term. Curvature-driven diffusion-based techniques, in particular Perona-Malik, anisotropic diffusion, mean curvature motion (MCM), affine invariant MCM (AIMCM), are employed as a regularization term in this optimal control formulation. Adopting the steepest-descent marching with an artificial time t, Euler-Lagrange (EL) equations with homogeneous Neumann boundary conditions are obtained. These EL equations are approximately solved by the explicit Petrov-Galerkin scheme. The method is applied to the registration of brain MR images of size 257×257. Computational results indicate that all these regularization terms produce similarly good registration quality but that the cost associated with the AIMCM approach is, on average, less than that for the others. SN - 1687-1847 UR - https://doi.org/10.1186/1687-1847-2012-193 DO - 10.1186/1687-1847-2012-193 ID - Akinlar2012 ER -