Presentation and summary of the paper: Retrieval and Ranking of Biomedical Images using Boosted Haar Features, Chandan K. Reddy and Fahima A. Bhuyan Abstract of the paper: Abstract— Retrieving similar images from large repository of heterogeneous biomedical images has been a difficult research task. In this paper, we develop a retrieval system that uses Haar features as its weak classifiers and builds strong training models using the adaboost algorithm. Our system is trained for each image category separately and the final boosted model is stored during the training phase. In the test phase, the most similar images for a given query image are computed using these boosted models. The main advantages of the proposed system are (1) cheap computation of the most relevant features for each image category and (2) fast retrieval of similar images for a given query image. Using performance metrics such as sensitivity and specificity, our results demonstrate the robustness and accuracy of the proposed system.

1. Image
Analysis
and
Interpretation
28/06/13
Melanie
Torres
Bisbal

2. ¡ Ranking
and
retrieval
of
medical
images
¡ Retrieve
information
from
a
database
that
the
images
are
similar
is
much
difficult
¡ Paper
proposed
a
new
algorithm
with
the
following
concepts:
§ Integral
image
§ Haar
like
features
§ Adaboost

3. ¡ Extracting
and
understanding
the
structure
and
characteristics
of
medical
images
is
challenging
¡ A
typical
radiology
department
generates
between
100,000
to
10
million
images
per
year
¡ Applications
in
the
detection
and
classification
¡ Also
specific
applications
in
image
retrieval
with
pulmonary
nodules
¡ Retrieve
and
sort
the
information
in
real
time

4. ¡ Since
80
has
been
a
research
topic
¡ But
the
field
of
biomedical
imaging
is
in
a
very
early
stage
¡ Images
to
train
and
test
the
proposed
algorithm
are
taken
from
the
database
of
IRMA
(Image
Retrieval
in
Medical
Applications)
¡ Use
a
subset
of
images
to
train
the
different
categories
and
remove
Haar-­‐like
features
to
build
specific
models

5. ¡ One
of
the
biggest
problems
is
precisely
recover
the
characteristics
that
define
the
visual
similarity
of
the
anatomical
structure
of
the
different
categories
¡ Generally
have
used
co-­‐occurrence
matrix
of
gray,
Gabor
filters,
etc..
¡ In
this
paper
are
based
more
on
reducing
the
time
a
given
question
(query)

6. ¡ Haar-­‐like
features,
proposed
by
Viola
and
Jones
¡ Two
advantatges:
§ The
system
can
be
used
for
a
wide
range
of
biomedical
image
retrieval
as
a
tumor
§ Recovery
time
it
takes
significament
is
low
in
comparison
to
other
methods

8. ¡ The
key
steps
to
construct
the
algorithm
described
in
the
paper
are:
§ Efficient
extraction
of
simple
wavelets
(Haar)
§ Train
the
boosting
algorithm
applied
to
each
category
§ Calculate
the
closest
similarity
given
a
query

9. ¡ Efficient
computation
from
Integral
Image
¡ In
this
paper
implemented
using
the
Intel
OpenCV
@:
¡ The
features
are:

11. ¡ In
the
training
phase
boosting
applied
to
each
separate
category
to
find
the
weights
and
the
weak
classifiers
¡ For
a
query
in
the
test
phase
the
system
will
identify
the
class
it
belongs
to
and
return
the
top
ranking
images
repository
¡ To
look
at
the
results
is
calculated:

15. ¡ Chandan
K.
Reddy
and
Fahima
A.
Bhuyan,
Retrieval
and
Ranking
of
Biomedical
Images
using
Boosted
Haar
Features
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