Simultaneous Intensity Inhomogensity Correction, Registration and Segmentation of Anatomical Structures From Brain MR Images

Abstract

Accurate segmentation for magnetic resonance (MR) images is an essential step in quantitative brain image analysis, and hence has attracted extensive research attention. However, due to the existence of noise and intensity inhomogeneity, also named as bias field, many segmentation methods suffer from limited accuracy. This paper presents a novel variational framework for the registration, segmentation and bias estimation simultaneously. We first presented an improved segmentation model by using the intensity statistic distributions with different means and variances in local regions. The model can estimate the bias field meanwhile segmenting images. We also proposed an anisotropic non-rigid registration method by using the structure tensor information and nonlocal information to contain the information of the image details. Finally, we defined a coupled term to combine the segmentation and registration. The registration term can provide shape information as a prior to guide the segmentation and the segmentation term can provide the edge information to guide the registration. The segmentation and registration can obtain benefit from each other. Our statistical results on both synthetic and clinical images show that the proposed method can overcome the difficulties caused by noise and bias fields and obtain more accurate results.