When talking about deformable template models, people usually
think of snakes. However, snakes are just a kind of specific
template models, as shown in the survey of Jain and
Dubes [79], where deformable template models are
classified according to
Figure . This section briefly
describes the different categories explained in this survey.
There are two main trends on template models: those which deal with a rigid (fixed) template and the rest where this template varies. It should be noted that the former trend is composed by the early template matching approaches and, although they can be applied on some industrial applications, nowadays are less used. Despite this fact, the reviewed pattern matching approaches for mass segmentation belong to this class of algorithms.
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In contrast a deformable model is active in the sense that it is
able to adapt itself to fit the given data. Two different
sub-trends can be found in this direction: free-form models and
parametric models. The former models can represent any arbitrary
shape as long as some regularization constraint (continuity,
smoothness) is satisfied. The well known active contours
approaches (snakes) [85] are classified in this category.
Examples of snakes used in mass segmentation reviewed in
Chapter are the works of Kobatake et
al. [89] and Sahiner et
al. [156,158]. Both approaches used such
technique to refine a previous rough segmentation.
On the other hand, there are approaches that provide information
related to the shape of the object to the system. Typically, these
works firstly try to characterize the shape using a parametric
formula, and secondly define a set of deformation modes which let
the initial shape vary and adapt to the real images. As usually it
is difficult to find a parametric formula to describe the shape,
and therefore a prototype template is also used. The active shape
models algorithm [37] is the most common algorithm of
this category. In this algorithm, a database of manually segmented
images is necessary to construct the mean shape and find their
modes of deformation using PCA analysis. In a second step, a new
image is segmented using its gradient description and finding
which are the main deformed modes. However, an important drawback
of such algorithm is the tricky manual segmentation, marking the
same number of points at the same position. As is shown in
Figure it is even difficult to say that two
masses have similar positions, due to the large variation found in
masses.
Thus, according to Figure
the algorithm proposed in this work should be classified as a
prototype parametric deformable template matching algorithm. By
means of the application of the eigenfaces algorithm [180]
over a set of real masses, a template and its deformation's modes
are found. Subsequently, and using a Bayesian scheme, the
prototype is searched in the images. In contrast to Active Shape
Models, the initial database of our proposal can be easily
obtained from the different public mammographic databases, as only
a rough manual segmentation is needed. Concretely, only the centre
and the size of the masses are necessary as a starting point (just
the bounding box of the mass).