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Histogram-based normalization technique on human brain magnetic resonance images from different acquisitions

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机构: [1]Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China. [2]Research Center for Medical Image Computing, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China. [3]Department of Biomedical Engineering and Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China. [4]Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China. [5]Lui Che Woo Institute of Innovation Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China. [6]Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China. [7]School of Geoscience and Info-Physics, Central South University, Changsha, China. [8]Department of Radiology, The Second Hospital of Jilin University, Changchun, Jilin, China. [9]Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.
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关键词: Magnetic resonance imaging Intensity normalization Histogram normalization Noise estimator Brain template construction

摘要:
Background: Intensity normalization is an important preprocessing step in brain magnetic resonance image (MRI) analysis. During MR image acquisition, different scanners or parameters would be used for scanning different subjects or the same subject at a different time, which may result in large intensity variations. This intensity variation will greatly undermine the performance of subsequent MRI processing and population analysis, such as image registration, segmentation, and tissue volume measurement. Methods: In this work, we proposed a new histogram normalization method to reduce the intensity variation between MRIs obtained from different acquisitions. In our experiment, we scanned each subject twice on two different scanners using different imaging parameters. With noise estimation, the image with lower noise level was determined and treated as the high-quality reference image. Then the histogram of the low-quality image was normalized to the histogram of the high-quality image. The normalization algorithm includes two main steps: (1) intensity scaling (IS), where, for the high-quality reference image, the intensities of the image are first rescaled to a range between the low intensity region (LIR) value and the high intensity region (HIR) value; and (2) histogram normalization (HN), where the histogram of low-quality image as input image is stretched to match the histogram of the reference image, so that the intensity range in the normalized image will also lie between LIR and HIR. Results: We performed three sets of experiments to evaluate the proposed method, i.e., image registration, segmentation, and tissue volume measurement, and compared this with the existing intensity normalization method. It is then possible to validate that our histogram normalization framework can achieve better results in all the experiments. It is also demonstrated that the brain template with normalization preprocessing is of higher quality than the template with no normalization processing. Conclusions: We have proposed a histogram-based MRI intensity normalization method. The method can normalize scans which were acquired on different MRI units. We have validated that the method can greatly improve the image analysis performance. Furthermore, it is demonstrated that with the help of our normalization method, we can create a higher quality Chinese brain template.

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出版当年[2014]版:
大类 | 3 区 工程技术
小类 | 4 区 工程:生物医学
最新[2025]版:
大类 | 4 区 医学
小类 | 4 区 工程:生物医学
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出版当年[2013]版:
Q2 ENGINEERING, BIOMEDICAL
最新[2023]版:
Q3 ENGINEERING, BIOMEDICAL

影响因子: 最新[2023版] 最新五年平均 出版当年[2013版] 出版当年五年平均 出版前一年[2012版] 出版后一年[2014版]

第一作者:
第一作者机构: [1]Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China. [2]Research Center for Medical Image Computing, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China. [3]Department of Biomedical Engineering and Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.
通讯作者:
通讯机构: [1]Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China. [2]Research Center for Medical Image Computing, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China. [3]Department of Biomedical Engineering and Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China. [6]Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China. [8]Department of Radiology, The Second Hospital of Jilin University, Changchun, Jilin, China.
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