| FULLY AUTOMATIC FRAMEWORK FOR SEGMENTATION OF BRAIN MRI IMAGE |
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| 作者姓名： | 林盘 郑崇勋 杨勇 顾建文 |
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| 作者单位： | Key Laboratory of Biomedical Information Engineering of Education Ministry，Institute of Biomedical Engineering，Xi'an Jiaotong University，Xi'an 710049，China，Key Laboratory of Biomedical Information Engineering of Education Ministry，Institute of Biomedical Engineering，Xi'an Jiaotong University，Xi'an 710049，China，Key Laboratory of Biomedical Information Engineering of Education Ministry，Institute of Biomedical Engineering，Xi'an Jiaotong University，Xi'an 710049，China，Key Laboratory of Biomedical Information Engineering of Education Ministry，Institute of Biomedical Engineering，Xi'an Jiaotong University，Xi'an 710049，China |
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| 基金项目： | ThisresearchwassupportedbytheNationalNaturalScienceFoundationofChina(No.30000224and30000056) |
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| 摘 要： | Automaticbraintissuesegmentationfrommag neticresonanceimages(MRI)isofgreatimportance forresearchandclinicalstudyofmuchneurological pathology.Duringthepastdecade,theMRIhashad agreatimpactonthediagnosticimagingofmosthu manorgansystem.Thesegmentationofbrai…
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| 关 键 词： | 磁共振 水平集 马尔可夫随机域 图象分割 |
FULLY AUTOMATIC FRAMEWORK FOR SEGMENTATION OF BRAIN MRI IMAGE |
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| LIN Pan,Zheng Chongxun,Yang Yong,GU Jianwen.FULLY AUTOMATIC FRAMEWORK FOR SEGMENTATION OF BRAIN MRI IMAGE[J].Academic Journal of Xi’an Jiaotong University,2005,17(1):25-28. |
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| Authors: | LIN Pan Zheng Chongxun Yang Yong GU Jianwen |
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| Institution: | Key Laboratory of Biomedical Information Engineering of Education Ministry, Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an 710049, China |
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| Abstract: | Objective To propose an automatic framework for segmentation of brain image in this paper. Methods The brain MRI image segmentation framework consists of three-step segmentation procedures. First, Non-brain structures removal by level set method. Then, the non-uniformity correction method is based on computing estimates of tissue intensity variation. Finally, it uses a statistical model based on Markov random filed for MRI brain image segmentation. The brain tissue can be classified into cerebrospinal fluid, white matter and gray matter. Results To evaluate the proposed our method, we performed two sets of experiments, one on simulated MR and another on real MR brain data. Conclusion The efficacy of the brain MRI image segmentation framework has been demonstrated by the extensive experiments. In the future, we are also planning on a large-scale clinical evaluation of this segmentation framework. |
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| Keywords: | magnetic resonance level set method Markov random filed image segmentation |
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