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Classification and retrieval of endoscopic images from the clinical outcomes research initiative...
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Classification and retrieval of endoscopic images from the clinical outcomes research initiative (CORI) collection
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http://www.ohsu.edu/xd/education/library/services/theses-dissertations/rights-statement.cfm
Title
Classification
and
retrieval
of
endoscopic
images
from the
clinical
outcomes
research
initiative
(CORI)
collection
Creator.PersonalName
Kalpathy-Cramer
,
Jayashree
Thesis.Degree
M.S.
Thesis.Major
Biomedical Informatics
Thesis.DateDegreeAwarded
June
2009
Institution
Oregon Health & Science University
School
School of Medicine
Department
Dept. of Medical Informatics and Clinical Epidemiology
Thesis.Advisor/Mentor
Hersh, William R.
Thesis.Committee
Logan, Judith R.
Song, Xubo B.
Subject.MeSH
Information Systems
Libraries, Digital
Database Management Systems
Pattern Recognition, Automated
Call Number
Q171 K142 2009
Description.Abstract
There has been a
substantial
growth
in the
number
of
images
being
created
every
day
in
healthcare
settings
.
Effective
image
annotation
and
retrieval
can
be
useful
in the
clinical
care
of
patients
,
education
and
research
.
Traditionally
,
image
retrieval
systems
have been
text-based
,
relying
on the
annotations
or
captions
associated
with the
images
.
Although
text-based
information
retrieval
methods
are
mature
and
well-researched
, they are
limited
by the
quality
and
availability
of the
annotations
associated
with the
images
.
Advances
in
techniques
in
computer
vision
have
led
to
methods
for
using
the
image
itself
as the
search
entity
. The
goal
of
our
project
was to
create
an
image
retrieval
system
a
set
of
1500
upper
endoscopic
images
from the
Clinical
Outcomes
Research
Initiative
Collection
.
We
have
created
a
web-based
multimodal
image
retrieval
system
written
using
the
Ruby
on
Rails
framework
.
Ferret
, a
ruby
port
of
Lucene
was
used
for the
text
indexing
of the
annotations
for the
text-based
retrieval
.
Our
database
also
contains
a
number
of
visual
features
created
using
image
processing
algorithms
that
allows
users
to
perform
content-based
retrieval
.
When
operating
in a
“query-by-example”
mode
,
our
system
retrieves
an
ordered
set
of
images
from the
test
collection
that are
“similar”
in
visual
content
to the
image
being
queried
.
We
also
evaluated
the
performance
of a
variety
of
image
features
and
machine
learning
classifiers
that
can
be
used
to
automatically
annotate
the
image
with an
image
class
consisting
of
one
of
eight
findings
.
We
developed
a
hybrid
algorithm
for
image
classification
that
showed
improved
performance
compared
to
commonly-used
classification
algorithms
. This
enabled
us to
provided
text-based
querying
capability
where
search
words
from a
controlled
vocabulary
retrieve
a
set
of
pre-classified
and
annotated
images
matching
the
search
criteria
.
Our
intention
was to
enable
users
to
query
using
either
a
sample
image
,
keywords
or
desired
image
class
to
retrieve
“similar
images”
from the
system
,
along
with a
display
of the
associated
information
from these
images
.
Although
CBIR
has
great
potential
in
patient
care
,
research
and
education
,
purely
content-based
image
retrieval
can
be
quite
challenging
for
clinical
purposes
due
to the
semantic
gap
.
Low
level
global
features
like
color
and
texture
may
not be
sufficient
for
classification
of
findings
.
However
,
combining
visual
and
textual
information
can
greatly
improve
retrieval
performance
.
Additionally
, the
use
of
distance
metric
learning
and
relevance
feedback
can
help
the
system
produce
results
that are
more
relevant
to the
user
.
Language
eng
Type
Text
Format.Use
Needs Adobe Acrobat to view
Format.FileSize
1140865 Bytes
OCLC number
438294184
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