Biomedical Engineering Reference
In-Depth Information
evolution equations (e.g., expression (7.14)), and using the
processing list
to
guarantee locality during evolution.
Other interesting perspectives for our work are out-of-core implementations
of other techniques such as region growing (for segmentation) and level sets (for
surface reconstruction).
To show this, let us consider a simple region growing algorithm, which
takes a seed point
p
, and find out the connected set:
{
q
∈
Image
;
|
I
(
q
)
−
I
(
p
)
|≤
}
. At run-time, we traverse the interval tree and find the active meta-
cells. Then, we fill the data cache and perform usual region growing opera-
tions [40], but calling
insert neighbor s
for each point
p
incorporated to the
region.
Besides, level sets can be made out-of-core by using the
narrow-band
tech-
nique described above. In this case, it is just a matter of observing that the level
sets algorithm would only need the image information inside the narrow band.
Henceforth, an out-of-core implementation can be provided.
7.11 Conclusions
Deformable models offer an attractive approach for geometry recovery and
tracking because these models are able to represent complex and broad shapes
variability, particularly in the context of medical imaging.
Despite their capabilities, traditional deformable models suffer from the
strong sensitivity to the initial contour position and topological limitations.
Among the possibilities to address these problems, we follow the research
line that uses a two-step approach: Firstly, a rough approximation of the
boundary is taken. Secondly, the obtained geometry is improved by a topo-
logically adaptable deformable model. The reconstruction method presented in
Section 7.5 is a result of our research in this direction.
We have used the T-surfaces model but it is pointed out that level sets could
also be used. When T-surfaces stops, we can discard the grid and evolve the
model without it to avoid errors due to the projections. Now, GVF can be useful
to improve the convergence toward the boundary.
Also, when using deformable surfaces, memory limitations can lower the
performance of segmentation applications for large 3D images. Few works have
been done to address this problem.
