Abstract #2473
A Hybrid Approach to Intensity Normalization of Brain MRI based on Gaussian Mixture Model and Histogram Matching
Xiaofei Sun 1 , Lin Shi 2,3 , Yishan Luo 1 , Winnie CW Chu 1 , and Defeng Wang 1,4
1
Department of Imaging and Interventional
Radiology, The Chinese University of Hong Kong, Shatin,
NT, Hong Kong,
2
Department
of Medicine and Therapeutics, The Chinese University of
Hong Kong, Shatin, NT, Hong Kong,
3
Chow
Yuk Ho Technology Centre for Innovative Medicine, The
Chinese University of Hong Kong, Shatin, NT, Hong Kong,
4
Department
of Biomedical Engineering and Shun Hing Institute of
Advanced Engineering, The Chinese University of Hong
Kong, Shatin, NT, Hong Kong
Intensity of similar tissues on brain MRIs is often
inhomogeneous because of the various acquisitions. It is
problematic since the analysis of MR images
(registration, segmentation and volumes statistics) may
depend on the hypothesis that corresponding anatomical
locations have a similar intensity level. In this study,
a new hybrid approach based on Gaussian mixture model
and histogram matching to normalize for intensity
differences on MR images is presented. This method does
not require spatial alignment. The effectiveness of
intensity normalization is validated on real data, and
the results show that intensity normalization
significantly improves the accuracy of tissues
segmentation results.
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