Segmentation of brain parenchyma and cerebrospinal-fluyid in multispectral magnetic-resonance images

This paper presents a new method to segment brain parenchyma and
cerebrospinal fluid spaces automatically in routine axial spin echo
multispectral MR images. The algorithm simultaneously incorporates
information about anatomical boundaries (shape) and tissue signature
(grey scale) using a priori knowledge. The head and brain are divided
into four regions and seven different tissue types. Each tissue type c
is modeled by a multivariate Gaussian distribution N(mu(c), Sigma(c)).
Each region is associated with a finite mixture density corresponding
to its constituent tissue types, Initial estimates of tissue parameters
{mu(c), Sigma(c)}(c=1,...,7) are obtained from L-means clustering of a
single slice used for training. The first algorithmic step uses the
EM-algorithm for adjusting the initial tissue parameter estimates to
the MR data of new patients, The second step uses a recently developed
model of dynamic contours to detect three simply closed nonintersecting
curves in the plane, constituting the arachnoid/dura mater boundary of
the brain, the border between the subarachnoid space and brain
parenchyma, and the inner border of the parenchyma toward the lateral
ventricles, The model, which is formulated by energy functions in a
Bayesian framework, incorporates a priori knowledge, smoothness
constraints, and updated tissue type parameters, Satisfactory maximum a
posteriori probability estimates of the closed contour curves defined
by the model were found using simulated annealing.