  Brain tumor segmentation from Magnetic Resonance Images (MRIs) is an
important task to measure tumor responses to treatments. However, automatic
segmentation is very challenging. This paper presents an automatic brain tumor
segmentation method based on a Normalized Gaussian Bayesian classification and
a new 3D Fluid Vector Flow (FVF) algorithm. In our method, a Normalized
Gaussian Mixture Model (NGMM) is proposed and used to model the healthy brain
tissues. Gaussian Bayesian Classifier is exploited to acquire a Gaussian
Bayesian Brain Map (GBBM) from the test brain MR images. GBBM is further
processed to initialize the 3D FVF algorithm, which segments the brain tumor.
This algorithm has two major contributions. First, we present a NGMM to model
healthy brains. Second, we extend our 2D FVF algorithm to 3D space and use it
for brain tumor segmentation. The proposed method is validated on a publicly
available dataset.
