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阿尔茨海默病患者MR图像偏差场修正和脑组织分割的研究
引用本文:周震,童隆正,李宁,于春水.阿尔茨海默病患者MR图像偏差场修正和脑组织分割的研究[J].北京生物医学工程,2009,28(1):39-42.
作者姓名:周震  童隆正  李宁  于春水
作者单位:1. 首都医科大学生物医学工程学院,北京,100069
2. 首都医科大学宣武医院,北京,10053
基金项目:国家自然科学基金,北京市自然科学基金 
摘    要:目的探索适用于阿尔茨海默病(Alzheimer’s disease,AD)患者MR图像脑组织的分割的方法。方法结合阿尔茨海默病患者MR图像中组织区域和边缘的特性对传统水平集进行改进,利用同态滤波对图像进行偏差场修正,增加了UNSHARP MASK处理方法,有效避免了水平集边界泄漏问题。结果标准体膜和真实数据实验证实,该改进算法分割结果优于SPM5。结论利用修正偏差场和添加UNSHARP MASK方法有可能提高AD患者MR图像脑组织分割的准确性和鲁棒性,本研究为MR图像脑组织的精确分割和进一步准确测量作了有益探索。

关 键 词:核磁共振  阿尔茨海默病  水平集  医学图像分割

Bias Field Correction and Segmentation in MR Image of Patients with Alzheimer's Disease
ZHOU Zhen,TONG Longzheng,LI Ning,YU Chunshui.Bias Field Correction and Segmentation in MR Image of Patients with Alzheimer's Disease[J].Beijing Biomedical Engineering,2009,28(1):39-42.
Authors:ZHOU Zhen  TONG Longzheng  LI Ning  YU Chunshui
Institution:ZHOU Zhen, TONG Longzheng,LI Ning, YU Chunshui( 1 Biomedical Engineering College, Capital University of Medical Sciences, Beijing 100069; 2 Xuanwu Hospital, Capital University of Medical Sciences, Beijing 100053)
Abstract:Purpose To explore a medical image segmentation method based on the brain tissue segmentation in MR image of Alzheimer's disease patients. Methods According to brain region and margin features of patients with AD, the traditional level set was improved. Applying unsharp mask process and bias field correction with .homorphic filter can avoid edge leakage effectively. Results Experiments for the segmentation on phantom and real data by improved algorithm, showed the validity and accuracy in image segmentation. The results were better than those of SPM5. Conclusions The developed level set showed the validity and accuracy in image segmentation. This method is useful for Alzheimer's disease patients' MR image analysis and measurement on brain tissues in future.
Keywords:MR  Alzheimer's disease  level set  medical image segmentation
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