Topology-Based Nonlocal Fuzzy Segmentation of Brain MR Image With Inhomogeneous and Partial Volume Intensity

Abstract

The aim was to automatically segment brain magnetic resonance (MR) image with inhomogeneous and partial volume (PV) intensity for brain and neurophysiology analysis. Rather than assuming the presence of a single bias field over the image data, we first apply a local model to MR image analysis. With the brain topology knowledge, several specific local regions are selected, and typical brain tissues are then extracted for the prior estimation of fuzzy clustering center and member function. A new nonlocal fuzzy labeling scheme is applied to global optimization segmentation based on the block comparison and distance weight, which is robust to noise and inhomogeneous intensity. The nonlocal labeling provides optimized fuzzy member value and local intensity estimation of brain tissues such as cerebrospinal fluid (CSF), white matter (WM), and gray matter (GM). In addition to inhomogeneous intensity, PV may lead to error segmentation. To correct error segmentation because of PV, this article also provides two correction schemes. The first one is to extract CSF in deep sulci, which captures more CSF candidate by intensity comparison and topology shape comparison. The local pure CSF, WM, and GM is then estimated to correct the interfaces of CSF/GM and WM/GM. The segmentation experiments are performed on both brainweb-simulated images and Internet brain segmentation repository database (IBSR) real images. The experimental results demonstrate the robust and efficient performance of our approach. Our approach can be applied to automatic segmentation of the brain MR image.