An automated and simple method for brain MR image extraction

The method uses an improved geometric active contour model which can not only solve the boundary leakage problem but also is less sensitive to intensity inhomogeneity. The method defines the initial function as a binary level set function to improve computational efficiency. The method is applied to both our data and Internet brain MR data provided by the Internet Brain Segmentation Repository.The results obtained from our method are compared with manual segmentation results using multiple indices. In addition, the method is compared to two popular methods, Brain extraction tool and Model-based Level Set.The proposed method can provide automated and accurate brain extraction result with high efficiency.Brain extraction which segments magnetic resonance (MR) head images into brain and non-brain region is often required for analyses of neuroimage data. Accurate and automated brain extraction plays an important role in the analyses because brain region should be isolated before other processing algorithms such as tissue classification, registration or cortical surface reconstruction can be made [1-3]. For example, functional images such as Functional magnetic resonance image (FMRI) and Positron Emission Tomography (PET) image usually contain few non-brain tissues, whereas high resolution MR images often contain some non-brain tissues(i.e., skin, fat, muscle, etc.), and if the non-brain tissues of MR images can be accurately removed beforehand, the registration robustness will be improved greatly [2]. Furthermore, as a pre-processing step, brain extraction is usually performed before a full segmentation of the brain region into grey matter (GM), white matter (WM), and cerebrospinal fluid (CSF), so that the segmentation problem can be simplified [4,5]. On the other hand, brain extraction is also a difficult and time-consuming pre-processing step performed in neuroimage analysis due to the complexity of human brain anatomy and weak boundaries between brain and non-brain t