3D Model-based segmentation of individual brain structures
            from magnetic resonance imaging data

                   D. Louis Collins

           Department of Biomedical Engineering
              McGill University, Montreal

     A Thesis submitted to the Faculty of Graduate Studies
     and Research in partial fulfillment of the requirements
     for the degree of Doctor of Philosophy.
     copyright D.L. Collins, October 1994.


                           ABSTRACT

This thesis addresses a specific problem of model-based segmentation;
namely, the automatic identification and delineation of gross
anatomical structures of the human brain based on their appearance in
magnetic resonance images (MRI).  The approach developed in this
thesis depends on a general, iterative, hierarchical registration
procedure and a 3-D digital model of human brain anatomy that contains
both volumetric intensity-based data and geometric atlas data that
co-exist in a brain-based stereotaxic coordinate system.  The model
contains features derived from an MRI atlas of gross neuroanatomy,
that is the result of an intensity average of 305 brains created with
an automatic stereotaxic registration procedure developed here.

The objective of this thesis is achieved by inverting the traditional
segmentation strategy.  Instead of matching geometric contours from an
idealized atlas directly to the MRI data, segmentation is achieved by
identifying the spatial transformation that, under certain
constraints, best maps corresponding features between the model and a
particular volumetric data set.  After automatic recovery of the
linear registration transform, the 3-D non-linear transformation is
recovered by estimating the local deformation fields, recursively
selected by stepping through the entire target volume in a 3D grid
pattern, using cross-correlation of invariant intensity features
derived from image data.  This registration process is performed
hierarchically, with each step in decreasing scale refining the fit of
the previous step and providing input to the next.  When completed,
atlas contours defined in the model are mapped through the recovered
transformation to segment structures in the original data set and
identify them by name.

Experiments for registration and segmentation are presented using
simple phantoms, a realistic digital brain phantom as well as human
MRI data.  The algorithm is used to estimate neuro-anatomical
variability, to automatically segment cerebral structures and to
create probabilistic representations of the same structures.
Validation with manual methods shows that the procedure performs well,
is objective and its implementation robust.



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