Elektronika 2009-11.pdf - Instytut Systemów Elektronicznych

Medical pattern intelligent recognition based
on linguistic modelling of 3D coronary vessels
visualisations
(Lingwistyczne modelowanie przestrzennych rekonstrukcji unaczynienia
wieńcowego w inteligentnym rozpoznawaniu zmian patologicznych)
mgr inż. MIROSŁAW TRZUPEK, prof. dr hab. MAREK R. OGIELA,
prof. dr hab. inż. RYSZARD TADEUSIEWICZ
AGH University of Science and Technology, Institute of Automatics, Bio-Cybernetics Laboratory, Krakow
Characteristic for the coronary vessels in different patients with
various pathologic forms observed on flat, i.e. 2D, images is a
certain level of repetitiveness and regularity. The same images
registered by a machine rendering 3D images (helical CT
scanner) feature a much greater number of visible details,
which, however, results also in the increase of the number of
both individual differences and those between various examinations
of the same patient. Having considering these difficulties,
a decision was made to apply the methods of automatic
image understanding for the interpretation of the images considered,
which consequently leads to their semantic descriptions.
Thanks to such posing of the problem, the diagnosis put
forth (being naturally but a suggestion for the physician making
the actual decision) will account for a greater number of
factors and use better the available medical information.
Additionally, it is worth pointing out that - even though this
is not the main focus of the work presented here - that the solution
of problems related to the automatic production of intelligent
(cognitive) descriptions of the 3D medical images in
question may be a significant contribution to solving at least
some of the problems connected to the smart archiving of this
type of data and the finding of semantic image data meeting
the semantic criteria delivered through sample image patterns
in medical multimedia databases.
The goal of research and description
of the problem
Problems of modelling biomedical structure shapes are extremely
interesting from the scientific point of view, but also in
connection with the development of medical diagnostic support
systems. They concern various structures diagnosed
using various modalities. However, in such research, we extremely
frequently see the use of an approach based on neutral
networks for shape modelling. For instance, such models
can be built using Kohonen networks [1]. However, it is worth
noting that such solutions are usually limited by the network
topology, which, although it allows you to operate on vectors
of features informative for the objects researched, does not
allow the image to be analysed or the organ to be reconstructed
in a holistic fashion, i.e. taking into account the full
dimensions of the image [2-4]. This only becomes possible
when we create linguistic descriptions using image grammars
based on graph formalisms. By definition, such grammars
have been dedicated for analysing image scenes, which in the
case of medical visualisation means that they can be used to
model multi-object structures or to conduct the meaning analysis
of spatial reconstructions of heart tomograms, which will be
demonstrated later in this publication.
Graph-based semantic models
of the heart’s coronary vessels
In order to analyze a 3D reconstruction (visualisations for various
patients obtained during diagnostic examinations of the
heart with a helical CT scanner with 64 detectors), it becomes
necessary to select the appropriate projection showing lesions
in vessels in a way that enables them to be analysed on
a plane. In our research we have attempted to automate the
procedure of finding such a projection by using selected geometric
transformations during image processing. Next, to enable
a linguistic representation of the spatial reconstructions
studied, the coronary vessels shown in them had been subjected
to the operation of thinning, referred to as skeletonizing.
This operation allows us to obtain a skeleton of the arteries
under consideration with the thickness of one unit. This skeleton
can then be subjected to the operation of labelling, which
determines the start and end points of main and surrounding
branches of coronary arteries in it. These points will constitute
the peaks of a graph modelling the spatial structure of the
coronary vessels of the heart.
The next step is labeling them by giving each located informative
point the appropriate label from the set of peak labels
which unambiguously identify individual coronary arteries
forming parts of the structure analysed. For the left coronary
artery they have been defined as follows: LCA - left coronary
artery, LAD - anterior interventricular branch (left anterior descending),
CX - circumflex branch, L - lateral branch, LM - left
marginal branch. And for the right coronary artery they have
been defined as follows: RCA - right coronary artery, A - atrial
branch, RM - right marginal branch, PI - posterior interventricular
branch, RP - right posterolateral branch. This way, all
initial and final points of coronary vessels as well as all points
where main vessels branch or change into lower level vessels
have been determined and labelled as appropriate. After this
operation, the coronary vascularization tree is divided into
sections which constitute the edges of a graph modelling the
examined coronary arteries. Mutual spatial relations that may
occur between elements of the vascular structure represented
by a graph are described by the set of edge labels. The elements
of this set have been defined by introducing the appropriate
spatial relations; vertical - defined by the set of labels
α, β,…, µ and horizontal - defined by the set of labels 1, 2,…,
24 on a hypothetical sphere surrounding the heart muscle.
These labels designate individual final intervals, each of which
has the angular spread of 15°. Then, depending on the location,
terminal edge labels are assigned to all branches identified
by the beginnings and ends of the appropriate sections of
coronary arteries.
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