A HYPER-SURFACE-BASED MODELING AND CORRECTION OF BIAS FIELD IN MR IMAGES

Abstract

Dealing with the different artifacts in medical images is necessary to perform several tasks, in particular segmentation and registration. We introduce in this paper a novel method for bias field correction in Magnetic Resonance Images (MRI). Using the segmentation results obtained by a modified Expectation Maximization clustering, the bias field is fitted as an hyper-surface in a 4D hyper-space. Then, it is corrected based on the fact that voxels belonging to the same tissue should have the same intensity in the whole image. So, after a quick and coarse unsupervised voxel labeling by clustering by parts is performed, the bias field is computed at the voxels that have been reliably labeled as belonging to one of the tissues of interest. For the less reliably labeled voxels the bias field is interpolated using an hyper-surface, estimated by a 4D lagrangian interpolation. In order to evaluate the efficiency of the proposed method, we compare the MRI segmen- tation results with and without bias field correction, and we use also the coefficient of variations within the MRI volume.Segmentation results, and the coefficient of variations results were significantly enhanced after bias field correction by the proposed method. Furthermore, the proposed method is considered fast compared to other methods, that are often time consuming du to their iterative nature.