MRI brain tissues segmentation using non-parametric technique

This paper presents a fully-automatic and robust MRI segmentation method for brain tissues. The proposed method classifies the brain MRI volume to 4 classes: white matter tissue (WM), gray matter tissue (GM), cerebrospinal fluid (CSF), and the remaining tissues as non-brain tissues (NBT). We utilize...

Full description

Saved in:
Bibliographic Details
Published in2008 International Conference on Computer Engineering & Systems pp. 185 - 190
Main Authors El-Melegy, M., Hasan, Y., Mokhtar, H.
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2008
Subjects
Biomedical imaging
Brain modeling
Clustering algorithms
Image segmentation
Magnetic resonance imaging
Medical simulation
Probability
Robustness
Solid modeling
Statistics
Online AccessGet full text

Cover

More Information
Summary:This paper presents a fully-automatic and robust MRI segmentation method for brain tissues. The proposed method classifies the brain MRI volume to 4 classes: white matter tissue (WM), gray matter tissue (GM), cerebrospinal fluid (CSF), and the remaining tissues as non-brain tissues (NBT). We utilize the pre-segmented volumes to determine statistically the prior probability for each class, prior information of the spatial locations of the voxels in the class, and also the intensity of each voxel. Parzen window is used to estimate non-parametrically the PDF of the prior information. Bayes rule is used to find the maximum posterior probability for each voxel. Experiments on real and simulated data demonstrate the advantages of the method over the recent methods. Several experimental results are reported.
ISBN:1424421152
9781424421152
DOI:10.1109/ICCES.2008.4772993