In this paper, we propose a new skull stripping method for T1-weighted magnetic resonance (MR) brain images. Skull stripping has played an important role in neuroimage research because it is a basic preliminary step in many clinical applications. The process of skull stripping can be challenging due to the complexity of the human brain, variable parameters of MR scanners, individual characteristics, etc. In this paper, we aim to develop a computationally efficient and robust method. In the proposed algorithm, after eliminating the background voxels with histogram analysis, two seed regions of the brain and non-brain regions were automatically identified using a mask produced by morphological operations. Then we expanded these seed regions with a 2D region growing algorithm based on general brain anatomy information. The proposed algorithm was validated using 56 volumes of human brain data and simulated phantom data with manually segmented masks. It was compared with two popular automated skull stripping methods: the brain surface extractor (BSE) and the brain extraction tool (BET). The experimental results showed that the proposed algorithm produced accurate and stable results against data sets acquired from various MR scanners and effectively addressed difficult problems such as low contrast and large anatomical connections between the brain and surrounding tissues. The proposed method was also robust against noise, RF, and intensity inhomogeneities.
All Science Journal Classification (ASJC) codes
- Cognitive Neuroscience