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| A Robust Method for Segmentation of Human Brain Tissue from Magnetic Resonance Images |
| Lin Pan;Zheng ChongXun;Yang Yong;Yan XiangGuo;Gu JianWen |
| Institute of Biomedical Engineering, Xi’an Jiaotong University,Xi ’an 710049, China |
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Abstract Automatic segmentation of brain magnetic resonance images is a critical problem in many medical imaging applications. In this paper, a robust automated segmentation algorithm is presented for the brain magnetic resonance images. The segmentation framework is composed of three stages. First, it uses level set method to perform the brain stripping operation. In the second stage, it compensates for nonuniformity in the brain image based on computing estimates of tissue intensity variation. Finally, a maximum aposteriori classifier is used to partition the brain into gray matter, white matter, and cerebrospinal fluid. The proposed method has been tested using magnetic resonance dada. This algorithm may be applied to various research and clinical investigations in which brain segmentation and volume measurement involving Magnetic resonance images dada are needed.
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Received: 15 September 2004
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2005, Vol. 27 
