In this work, we use the geometric information, such as edges and thin structures, to build a similarity measure for deformable registration models of multi-modality images. The idea is to extract a geometric information from the images and then use it to build a robust and efficient similarity term. In order to extract this information, we use the Blake-Zisserman's energy that is well suited for detecting discontinuities at different scales, i.e. of first and second order. In addition, we present a theoretical analysis of the proposed model. For the numerical solution of the model, we use a gradient descent method and iteratively solve corresponding the Euler-Lagrangian equation. We present some numerical results that demonstrate the efficiency of the proposed model.