Biomedical Engineering Reference
In-Depth Information
curves/surfaces are in general not smooth, presenting defects such as protru-
sions, concavities, or even holes (for surfaces) due to image irregularities.
These problems can be addressed through an efficient presegmentation.
For instance, when reconstructing the geometry of the human cerebral cor-
tex, Prince
et al.
[76] used a fuzzy segmentation method (
Adaptive Fuzzy C-
Means
) to obtain the following elements: a segmented field which provides a
fuzzy membership function for each tissue class; the mean intensity of each
class; and the inhomogeneity of the image, modeled as a smoothly varying gain
field (see [76] and references therein). The result can be used to steer the iso-
surface extraction process as well as the deformable model, which is initial-
ized by the obtained isosurface. We have used a similar approach as described
in [33].
Moreover, the image forces may not be strong enough to push the model
toward the object boundary. Even the balloon model in Eq. (7.9) cannot deal
with such a problem because it is difficult to predict if the target is inside or
outside the isosurface (see Fig. 7.6). So, it makes harder to accurately define the
normal force field. The GVF (Section 7.8) can be used to generate an image force
field that improves the convergence of the model toward the boundary. GVF is
sensitive to noise and artifacts but we can achieve good results for presegmented
images [77, 78].
Now, we will compare our segmentation approach (Section 7.5) to that pro-
posed in [47]. In that reference, a set of small spherical T-snakes is uniformly
distributed over the image. These curves progressively expand/merge to recover
the geometry of interest. The same can be done for 3D.
Our approach can be considered an improvement of that one described in
[47]. Our basic argument is that we should use the threshold to get
seeds
closer
to the objects of interest. Thus, we avoid expanding time evolving surfaces
far from the target geometry. Besides, we have observed an improvement in
the performance of the segmentation process if compared with the traditional
initialization of T-surfaces (an implicit defined surface inside the object) [49].
Our method is adaptive in the sense that we can increase the T-surfaces grid
resolution where it is necessary. As the T-surfaces grid controls the density of
the polygonal surfaces obtained, the number of triangular elements gets larger
inside these regions. That increase in density is not due to boundary details but
because the outer scale corresponding to the separation between the objects is
too fine (as in Fig. 7.9). This is a disadvantage of our approach.
