| Περίληψη: | Nowadays,hospitals are equipped with high resolution
medical
imaging
systems
such
as
MRI,
CT
that
help
the
radiologists
to
make
more
accurate
diagnosis.
However
these
systems
cannot
give
any
information
of
the
explicit
content
that
is
on
the
image
pixels.
The
vast
amount
of
images
that
are
produced
in
hospitals
is
processed
mainly
by
the
medical
domain
users.
Even
systems
such
as
PACS
cannot
retrieve
images
with
anatomical
or
disease-‐related
criteria.
The
integrating
of
semantic
web
technologies
in
health
care
can
provide
a
solution.
The
benefits
for
the
semantic
web
technologies
are
owed
to
the
core
element
of
the
semantic
web,
which
is
the
ontology.
The
ontology
sets
strict
relationships
between
its
entities.
The
main
goal
of
this
thesis
is
to
design
and
develop
an
online
approach
for
Semantic
Annotation
and
Retrieval
of
Medical
Images.
The
architecture
of
the
proposed
system
is
based
on
a
service
oriented
approach
that
enables
the
expandability
of
the
system
by
integrating
new
features
such
as
image
processing
algorithms
to
perform
Computer
Aided
Diagnosis
(CAD)
tasks
and
to
make
queries
with
low
-‐
level
image
characteristics.
Also
the
adopting
of
such
an
approach
for
the
architecture
allows
to
add
new
reference
ontologies
to
the
system
without
redesigning
the
core
architecture.
The
ontology
framework
of
the
system
includes
(a)
three
reference
ontologies,
namely
the
Foundational
Model
of
Anatomy
(FMA)
for
the
anatomy
annotation,
the
International
Classification
of
Disease
(ICD-‐10)
for
the
disease
annotation
and
the
RadLex
for
the
radiological
findings
and
(b)
an
application
ontology
that
connects
the
medical
document
with
the
concepts
of
the
medical
ontologies
(FMA,
ICD-‐10,
Radlex)
and
it
also
contains
information
about
patient,
hospital
and
image
modality.
Part
of
application
ontology
information
is
extracted
from
the
DICOM
header.
In
the
context
of
the
current
thesis,
the
system
was
used
to
annotate
and
retrieve
several
medical
images.
The
proposed
online
approach
for
annotation
and
retrieval
of
medical
images
system
can
enable
the
interoperability
between
different
Health
Information
Systems
(HIS)
and
can
constitute
a
tool
for
discovering
the
hidden
knowledge
in
medical
image
data.
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