DGtal
0.9.2

This part of the manual describes the DGtal volumetric module. We focus here on separable process based volumetric analysis such as distance transformation, reverse distance transformation and medial axis extraction.
For decades, distance transformation (DT) and geometrical skeleton extraction have been classic tools for shape analysis [57] [58] . The DT of a shape consists in labelling object grid points with the distance to the closest background pixel. From the DT values, we thus have information on the shape geometry. Beside its applications in shape description, DT has been used in many situations such as shape analysis, shape matching, shapebased interpolation, motion planning, image registration, or differential measurement estimation.
In the literature, many techniques have been proposed to compute the DT given a metric with a tradeoff between algorithmic performances and the accuracy of the metric compared to the Euclidean one. Hence, many papers have considered distances based on chamfer masks [57] [58] [9] , or sequences of chamfer distances; the vector displacement based Euclidean distance [24] [55] the Voronoi diagram based Euclidean distance [11] [45] or the square of the Euclidean distance [63] [35] . From a computational point of view, several of these methods lead to time optimal algorithms to compute the errorfree Euclidean Distance Transformation (EDT) for n dimensional binary images: the extension of these algorithms is straightforward since they use separable techniques to compute the DT; n onedimensional operations one per direction of the coordinate axis are performed.
In [17], it has been demonstrated that a similar decomposition can be used to compute both the reverse distance transformation and a discrete medial axis of the binary shape.
In fact, the separable decomposition and the associated algorithmic tools can be used on a wider class of metrics (see [35] or [45]). For instance, all weighted \(l_p\) metrics defined in \(R^n\) by
\[ d_{L_p} (u,v) = \left ( \sum_{i=0}^n w_iu_iv_i ^p \right )^{\frac{1}{p}}\]
can be considered.
In DGtal, we have chosen to implement such volumetric tools such that the underlying metric could be specified independently.
For a complete discussion of metric concepts in DGtal, please refer to Metric Spaces, Digital Metric Spaces and Related Concepts.
The generic distance transformation is based on a prior Voronoi map consturction. Indeed, if we compute the Voronoi diagram of background points, the distance transformation at an object point is exactly its distance to the site associated with the Voronoi cell it belongs to.
The core of the implementation is based on a separable approach: For example, in dimension 2, partial digital Voronoi maps of dimension one are first computed in each row independently. Then such partial Voronoi maps are updated using independent processes along the columns, leading to a valid Voronoi map of dimension 2. In an algorithmic point of view, the 1D processes used for both columns and rows are exactly the same. In higher dimensions, the other dimensions are processed similarly.
In DGtal, the class VoronoiMap implements such digital Vornoi map extraction. Such class is parametrized by the following types:
The VoronoiMap constructor is parametrized by
The VoronoiMap will be computed on the specified and will use the point predicate to decide if a point of such domain is in the object or note.
Once the VoronoiMap object is created, the voronoi map is computed and the class itself is a model of CConstImage. In other words, you can access to the VoronoiMap value at a point p and iterate of values using image ranges (see Images). For example
Since we are constructing a VoronoiMap, the value type of the map is a vector (pointing to the closest site) of type Space::Vector (if Space was the underlying digital space type used when specifying VoronoiMap template parameters).
Let us illustrate the construction in dimension 2 (see voronoimap2D.cpp). Other examples can be found in distancetransform2D.cpp and distancetransform3D.cpp.
First of all, we need couple of includes:
We will discuss later about the metric definition but let us consider a classical Euclidean \( l_2 \) metric:
We now consider an object in a [0,0]x[16,16] domain with three background points. To construct such point predicate, we first define a set containing the three points, then we consider the point predicate defined on this set (which returns true at a point if the point is inside the set) and we consider the negation of such predicate in order to return true for object points. Here you have the construction:
and the resulting set:
The voronoi map is simply given by:
At each point of the object, we thus have a vector to the closest background point. We can display this information as follows:
To obtain:
To get:
As discussed earlier, the distance transformation is given by computing distances once the Voronoi map is obtained. In DGtal, the class DistanceTransformation simply adapts the VoronoiMap class in order to override output image getters to return the distance for the given metric to the closest site instead of the vector.
As a consequence, the DistanceTransformation class simply inherits from the VoronoiMap class and overrides methods required by the concepts::CConstImage concept. Note that the DistanceTransformation::Value type is double. If you want to get the underlying vector instead of the distance to perform exact computations, you can use the DistanceTransformation::getVoronoiVector method.
In the following example, we consider the previous small image and use a colormap to display distance values for the \( l_2 \) mertic:
Similarly to Voronoi diagram and digital Voronoi maps, digital Power maps are defined as the intersection between the integer grid and a power diagram. Given a set of weighed points, power diagram can be seen as Voronoi diagram where the metric is modified with additive weights. For example, considering the \( l_2\) metric, the power distance between a point \(p\) and a weighted point \((q,w)\) is defined by
\[ pow(p,q) = \ p  q\_2^2  w \]
Hence, similarly to Voronoi diagram, the power diagram is a decomposition of the space ino cells from weighed sites where each cell (maybe empty) is associated with a site and each point in the cell has got minimal power distance to the cell site (compared to its power distance to all other sites) [5] .
Separable algorithms similar to VoronoiMap/DistanceTransformation can be designed to compute respectively PowerMap and ReverseDistanceTransformation. The only difference is that the input of PowerMap is a weighted set of points instead of a point predicate.
Hence such class is parametrized by the following types:
The PowerMap constructor is parametrized by
Similarly to DistanceTransformation, ReverseDistanceTransformation remaps the PowerMap vectors to map the power metric to the closest weighted site.
As a consequence, for the Euclidean \( l_2 \) metric, if we consider a set of balls \( B_i(p_i,r_i) \) and if we create an WeightImage whose domain contains points \(\{ p_i \}\) and with values \( r_i^2\), negative (strictly) values of the ReverseDistanceTransformation will correspond to digital points belonging to the union \( \bigcup \{B_i\}\) (see [17]).
ReverseDistanceTransformation can thus be used to reconstructed a binary shape from a given Medial Axis or any set of balls. Another consequence is that given a binary shape, the pipeline
\[ Shape \rightarrow DT \rightarrow ReverseDT \rightarrow \text{ strictly negative values }\]
for the same metric/power metric, returns the input binary shape.