title: Histogram-based normalization technique on human brain magnetic resonance images from different acquisitions.
abstract: 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.
authors.affiliation: Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.|Research Center for Medical Image Computing, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.|Department of Biomedical Engineering and Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.
authors.forename: Xiaofei
authors.initials: X
authors.lastname: Sun
authors.affiliation: Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.|Lui Che Woo Institute of Innovation Medicine, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.
authors.forename: Lin
authors.initials: L
authors.lastname: Shi
authors.affiliation: Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.|Research Center for Medical Image Computing, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.
authors.forename: Yishan
authors.initials: Y
authors.lastname: Luo
authors.affiliation: Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China.|School of Geoscience and Info-Physics, Central South University, Changsha, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Yang
authors.affiliation: Department of Radiology, The Second Hospital of Jilin University, Changchun, Jilin, China. lhp9019@163.com.
authors.forename: Hongpeng
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.
authors.forename: Peipeng
authors.initials: P
authors.lastname: Liang
authors.affiliation: Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.
authors.forename: Kuncheng
authors.initials: K
authors.lastname: Li
authors.affiliation: Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.
authors.forename: Vincent C T
authors.initials: VCT
authors.lastname: Mok
authors.affiliation: Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.|Research Center for Medical Image Computing, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China.|Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China.
authors.forename: Winnie C W
authors.initials: WCW
authors.lastname: Chu
authors.affiliation: Department of Imaging and Interventional Radiology, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China. dfwang@cuhk.edu.hk.|Research Center for Medical Image Computing, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China. dfwang@cuhk.edu.hk.|Department of Biomedical Engineering and Shun Hing Institute of Advanced Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China. dfwang@cuhk.edu.hk.|Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, China. dfwang@cuhk.edu.hk.
authors.forename: Defeng
authors.initials: D
authors.lastname: Wang
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods* / Q000592:standards; D008297:Male; D009929:Organ Size; D012015:Reference Standards; D055815:Young Adult