Computation of the mid-sagittal plane in 3D images of the brain

Abstract

We present a new symmetry-based method allowing to automatically compute, reorient and recenter the mid-sagittal plane in anatomical and functional 3D images of the brain. Our approach is composed of two steps. At first, the computation of local similarity measures between the two hemispheres of the brain allows to match homologous anatomical structures or functional areas, by way of a block matching procedure. The output is a set of point-to-point correspondences: the centers of homologous blocks. Subsequently, we define the mid-sagittal plane as the one best superposing the points in one side of the brain and their counterparts in the other side by reflective symmetry. The estimation of the parameters characterizing the plane is performed by a least trimmed squares optimization scheme. This robust technique allows normal or abnormal asymmetrical areas to be treated as outliers, and the plane to be mainly computed from the underlying gross symmetry of the brain. We show on a large database of synthetic images that we can obtain a subvoxel accuracy in a CPU time of about 3min utes, for strongly tilted heads, noisy and biased images. We present results on anatomical (MR, CT), and functional (SPECT and PET) images.