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Figure 1: Drosophila wing epithelial cells.
The following approach is thus developed both to
retrieve the network structure as a fine representation
and to abstract the information related to the structure
into a manageable model. This is obtained by explor-
ing the visual data through a dynamic random walk
model, whose motion state summarizes the useful in-
formation and allows the full detailed reconstruction
of the network. To give a pictorial idea, the image
frame is considered as a landscape where clear paths
have to be discerned from darker areas. The agent
performing the detection advances by scanning the
neighborhood of his current position in search for ex-
plorable paths. When a bifurcation occurs the agent
generates one or more siblings that start to move in-
dependently, until the whole frame has been explored.
The remainder of the paper is organized as fol-
lows. After an overviewof the state of the art in struc-
ture detection (Sec. 2) and a description of the pro-
cedure for image preprocessing (Sec. 3), the adopted
model is introduced in Sec. 4 and the algorithm ex-
plained in Sec. 5. Then, the static analysis of the im-
age frame is discussed in Sec. 6 and, finally, in Sec. 7
some conclusions are drawn and insight on future de-
velopments is given.
2 THE STATE OF THE ART
The recognition of structures in digital images is a
crucial task in many automated algorithms used in
computer vision. For the task of edge detection,
several approaches based on derivatives have been
proposed, among which the seminal studies by So-
bel (Sobel, 1968) and Prewitt (Prewitt, 1970) and
Canny (Canny, 1986).
These approaches though are prone to failure,
since they do not incorporate any prior knowledge of
the object, nor do they include any geometrical model.
This can yield very fragmented edges and many false
classifications. They also do not return any com-
pact and light representation of the recognized pat-
tern. Such algorithms are good to represents patterns,
but not to model them. The Canny edge detector out-
puts a bitmap map, where each pixel is classified as
belonging to a border or not. Indeed, no structure is
returned, and no compact model is given to the user.
If there is a need for computing metrics or for analyti-
cally following a path around the edges, this approach
appears to be unsatisfactory.
Active Contours (Blake and Isard, 1998) based
approaches give better results, thanks to the elastic
model structure they incorporate. Active Contours
and Deformable Models (McInerney and Terzopou-
los, 1996) generally perform well in shape recogni-
tion. Still, they are very sensitive to noise, they need a
good initialization point to converge, and they require
hard-tuning of the parameters to make things really
work.
For thin linear structures such as vessels, mar-
ble veins, roads on terrain, better results can be
achieved using a model of the motion over the im-
age, which attempts to follow the structure of interest
(Grisan et al., 2003). This approaches is promising
and achieves near optimal results. A random motion
paradigm can also be used also for image segmen-
tation (Harel and Koren, 2001) and image enhance-
ment (Smolka and Wojciechowski, 2001).
3 IMAGE PREPROCESSING
The images retrieved from biological experiments
are particularly noisy and exhibit poor contrast, with
non-uniform background illumination, resulting in
structure boundaries not sufficiently sharp to be seg-
mented. The preprocessing stage presented consists
of four sequential steps (see Fig.2):
1. the image is filtered with a low-pass gaussian filter
to soften high frequency noise;
2. an erosion filter suppresses the isolated bright pix-
els and decreases the intensity of cell edges, while
retaining all the significant information;
3. an histogram stretch allows to partially recover the
color dynamic range;
4. an image intensity power enhances the contrast.
4 THE DYNAMICAL MODELS
In this section the principle underlying the algorithm
is briefly described. To retrieve the salient structure in
the video sequence and to circumvent issues related
to disconnected edges and false recognition (such as
those mentioned in Sec. 2), the edge detection prob-
lem is re-interpreted as the problem of exploring a
digital frame in search of the connected tracks. Each
THE EMERGENT STRUCTURE OF THE DROSOPHILA WING - A Dynamic Model Generator
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