1) A new semi-automatic method is proposed to segment cortical gray matter from MRI scans in order to better visualize brain activity measured by fMRI. 2) The method involves 4 steps: segmenting white matter, selecting the white matter cortex component, verifying its topology matches anatomy, and growing gray matter classification out from the white matter boundary in a constrained way. 3) The semi-automatic method takes only 15 minutes compared to 18 hours for manual segmentation, and ensures the segmented gray matter is anatomically correct without tears or intersections.

1. Visualization of Cortical Activity Using fMRI
Patrick Teo, Guillermo Sapiro, and Brian Wandell*
Imaging Technology Department, HP Labs, and Stanford University*
Summer 1996
Sponsored by the Hewlett Packard Labs Grassroots Basic Research Program
Acknowledgements: Hagit Hel-Or (Stanford) for the original version of the segmentation tool. Steve Engel (Stanford) for
the brain unfolding software. Geoff Boynton and Jon Demb (Stanford) for expertise on anatomy and f/MRI. Tom
Malzbender (HP Labs) for discussions on graphics and visualization. Daniel Lee, Alex Drukarev, and the Grassroots
Research Program Committee (HP Labs) for supporting this research.
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2. Background
The goal of this summer project was to
investigate and to improve existing
techniques involved in the
visualization of human cortical activity
using functional magnetic resonance
imaging (fMRI). In conventional
magnetic resonance imaging (MRI), a
volume of data representing the
anatomical structure of the cortex is
reconstructed. Functional MRI, on the
other hand, indirectly measures the
amount of neural activity in different
regions of the cortex. Cortical activity
occurs largely in the gray matter
regions of the cortex. Thus, most Figure 1: MRI of the occipital pole of the
left hemisphere of a human brain.
visualization techniques require prior
segmentation of this gray matter
component.
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3. Background (cont’d)
Gray matter segmentation is difficult
because gray matter forms only a thin
(2 mm or 2 pixel) layer on the surface
of the cortex, which itself is inundated
with numerous ridges and folds (as
shown in Figure 2). Currently, much of
gray matter segmentation is
performed manually. This process is
tedious and requires several days even
for a trained person. On the other
hand, most automatic gray matter
segmentation methods that have been
proposed do not consider anatomical
constraints and often produce
segmentations that are anatomically
incorrect. We propose a new semi-
Figure 2: Photograph of a cross section of automatic method of gray matter
an actual human cortex. segmentation that takes into
consideration these anatomical
requirements.
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4. Method (Step 1)
Our proposed method consists of four
steps. In the first step, white matter is
segmented. White matter is
segmented because it is less noisy and
suffers less from partial volume
effects. Figure 3 shows the results of
segmenting the white matter in Figure
1. The maximum a posteriori
probability (MAP) estimate coupled
with a structural prior is used to
determine segmentation. The
structural prior is applied using a novel
technique involving anisotropic
smoothing.
Figure 3: Segmentation results of white
matter.
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5. Method (Step 2)
In the second step, the white matter
connected component corresponding
to the cortex is selected using a 3D
connected components algorithm.
After this single connected component
is extracted, the topology of that
component is verified in the third step.
Since, in the anatomy, gray matter
borders on white matter, the topology
of gray matter places constraints on
the topology of white matter.
Figure 4: Segmentation results of gray Specifically, because gray matter is a
matter. sheet, the white matter component
cannot have cavities or handles.
Cavities are identified using a flood-
filling algorithm. Handles are detected
by computing the Euler characteristic
of the white matter.
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6. Method (Step 3)
In the final step, the gray matter
classification is grown out from the
boundary of the white matter
component (as shown in Figures 4 and
5). Since gray matter is at most 2 mm
(or 2 pixel) thick in this direction, only
two layers of gray matter classification
are grown. Connectivity between gray
matter voxels are determined from the
connectivity of their parents from
which they were grown. Thus, gray
matter voxels may be adjacent in the
volume but not connected on the gray
matter surface.
Figure 5: Segmentation results of gray
matter overlaid on original MR image.
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7. Conclusions
Manual segmentation of the occipital
pole of the left hemisphere (less than
one eighth of the cortex) takes about
18 hours typically while the semi-
automatic procedure proposed here
requires only about 15 minutes.
Furthermore, the procedure ensures
that the gray matter is
topologically/anatomically correct in
that no tears or self-intersections
occurs. Once the highly convoluted
gray matter regions are segmented,
they can then be unfolded and
flattened so that regions hidden deep
in the folds become completely visible Figure 6: Flattened gray matter of the left
occipital lobe of a human cortex. Gray
(as shown in Figure 6). Functional MR matter was segmented semi-automatically
images can then be overlaid on this by the proposed algorithm. Colors
flattened representation so as to give a represent the 3D distance of the original
gray matter voxel from a fiducial plane.
better understanding of neural Brighter colors denote larger distances.
function in the different areas of the
cortex.
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8. Publications
• Anisotropic Smoothing of Posterior Probabilities.
Patrick C. Teo and Guillermo Sapiro and Brian A. Wandell.
Dynamical Systems, Control, Coding, Computer Vision : New Trends, Interfaces, and Interplay, Giorgio Picci
and David S. Gilliam, Editors, Progress in Systems and Control Theory, vol. 25, 1999.
• Creating connected representations of cortical gray matter for functional MRI visualization.
Patrick C. Teo and Guillermo Sapiro and Brian A. Wandell.
IEEE Transactions on Medical Imaging, vol. 16, no. 6, pp. 852-863, Dec. 1997.
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