Robust tissue-air volume segmentation of MR images based on the statistics of phase and magnitude: Its applications in the display of susceptibility-weighted imaging of the brain.
OBJECTIVE
To develop a robust algorithm for tissue-air segmentation in magnetic resonance imaging (MRI) using the statistics of phase and magnitude of the images.


METHODS
A multivariate measure based on the statistics of phase and magnitude was constructed for tissue-air volume segmentation. The standard deviation of first-order phase difference and the standard deviation of magnitude were calculated in a 3 x 3 x 3 kernel in the image domain. To improve differentiation accuracy, the uniformity of phase distribution in the kernel was also calculated and linear background phase introduced by field inhomogeneity was corrected. The effectiveness of the proposed volume segmentation technique was compared to a conventional approach that uses the magnitude data alone.


RESULTS
The proposed algorithm was shown to be more effective and robust in volume segmentation in both synthetic phantom and susceptibility-weighted images of human brain. Using our proposed volume segmentation method, veins in the peripheral regions of the brain were well depicted in the minimum-intensity projection of the susceptibility-weighted images.


CONCLUSIONS
Using the additional statistics of phase, tissue-air volume segmentation can be substantially improved compared to that using the statistics of magnitude data alone.
Department of Psychiatry, University of Colorado Denver, School of Medicine, Denver, Colorado 80045, USA. yiping.du@ucdenver.edu
Yiping P
YP
Du
Zhaoyang
Z
Jin
D000388:Air; D000465:Algorithms; D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*; D019047:Phantoms, Imaging; D013223:Statistics as Topic