We describe a new framework for measuring cortical thickness from MR human brain images. This involves the
integration of a method of tissue classification with one to estimate thickness in 3D. We have determined an additional
boundary detection step to facilitate this. The classification stage utlizes the Expectation Maximisation
(EM) algorithm to classify voxels associated with the tissue types that interface with cortical grey matter (GM,
WM and CSF). This uses a Gaussian mixture and the EM algorithm to estimate the position and and width
of the Gaussians that model the intensity distributions of the GM, WM and CSF tissue classes. The boundary
detection stage uses the GM, WM and CSF classifications and finds connected components, fills holes and then
applies a geodesic distance transform to determine the GM/WM interface. Finally the thickness of the cortical
grey matter is estimated by solving Laplace's equation and determining the streamlines that connect the inner
and outer boundaries. The contribution of this work is the adaptation of the classification and thickness measurement
steps, neither requiring manual initialisation, and also the validation strategy. The resultant algorithm
is fully automatic and avoids the computational expense associated with preserving the cortical surface topology.
We have devised a validation strategy that indicates the cortical segmentation of a gold standard brain atlas
has a similarity index of 0.91, thickness estimation has subvoxel accuracy evaluated using a synthetic image and
precision of the combined segmentation and thickness measurement of 1.54mm using three clinical images.
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