TELK
OMNIKA
,
V
ol.
17,
No
.
5,
October
2019,
pp
.
2587
-
2594
ISSN:
1693-6930,
accredited
First
Gr
ade
b
y
K
emenr
istekdikti,
Decree
No:
21/E/KPT/2018
DOI:
10.12928/TELK
OMNIKA.v17i5.11964
2587
Chest
radiograph
ima
g
e
enhancement
with
wa
velet
decomposition
and
morphological
operations
Anthon
y
Y
.
Aidoo
*1
,
Matilda
Wilson
2
,
Gloria
A.
Botc
hwa
y
3
1
Depar
tment,
of
Mathematical
Science
,
Easter
n
Connecticut
State
Univ
ersit
y
,
Willimantic
,
CT
06226,
USA
2
Depar
tment
of
Computer
Science
,
Univ
ersity
of
Ghana,
Legon,
Ghana
3
Depar
tment
of
Mathematics
,
Univ
ersity
of
Ghana,
Legon,
Ghana
*
Corresponding
author
,
e-mail:
aidooa@easter
nct.edu
1
,
matw
aa@ug.edu.gh
2
,
gaantwi@ug.edu.gh
3
Abstract
Medical
image
processing
algor
ithms
significantly
aff
ect
the
precision
of
disease
diagnostic
process
.
This
mak
es
it
cr
ucial
to
impro
v
e
the
quality
of
a
medical
image
with
the
goal
to
enhance
perceiv
ability
of
the
points
of
interest
in
ord
er
to
obtain
accur
ate
diagnosis
of
a
patient.
Despite
the
reliance
of
v
ar
ious
medical
diagnostics
on
X-r
a
ys
,
the
y
are
usually
plagued
b
y
dar
k
and
lo
w
contr
ast
proper
ties
.
Sought-after
details
in
X-r
a
ys
can
only
be
accessed
b
y
means
of
digital
image
processing
techniques
,
despite
the
f
act
that
these
t
echniques
are
f
ar
from
being
perf
ect.
In
this
paper
,
w
e
implement
a
w
a
v
elet
decomposition
and
reconstr
uction
technique
to
enhance
r
adiog
r
aph
proper
ties
,
using
a
ser
ies
of
mor
phological
erosion
and
dilation
to
impro
v
e
the
visual
quality
of
the
chest
r
adiog
r
aphs
f
or
the
detection
of
cancer
nodules
.
K
e
yw
or
ds:
chest
r
adiog
r
aph,
image
enhancement,
mathematical
mor
phology
,
w
a
v
elet
decomp
osition
Cop
yright
c
2019
Univer
sitas
Ahmad
Dahlan.
All
rights
reser
ved.
1.
Intr
oduction
A
chest
r
adiog
r
aph
pro
vides
a
g
reat
measure
of
medical
inf
or
mation
about
a
patient’
s
condition
per
taining
to
such
diseases
as
lung
cancer
and
chest
inf
ections
.
Ho
w
e
v
er
,
images
produced
b
y
X-Ra
ys
,
are
filled
with
noise
due
to
interf
e
rences
from
captur
ing
de
vices
and
anatomical
str
uctures
[1].
The
pr
ime
inspir
ation
in
the
v
ast
major
ity
of
the
computer
algor
ithms
f
or
helping
r
adiologists
in
e
xamining
chest
r
adiog
r
aph
imag
es
,
is
the
clinical
significance
of
chest
r
adiog
r
aph
[2].
Locating
cancer
nodules
in
chest
r
adiog
r
aphs
helps
to
detect
ear
ly
signs
of
lung
cancer
.
Ho
w
e
v
er
,
anatomical
str
uctures
usually
constitute
unw
anted
ar
tif
acts
in
captured
X-r
a
y
images
.
Due
to
the
siz
e
and
density
,
nodules
are
usually
difficult
to
detect
in
a
chest
r
adiog
r
aph
[3,
4].
Image
processing
algor
ithms
are
designed
to
impro
v
e
the
accur
acy
of
the
diagnostic
procedure
[5],
especially
,
in
applications
in
Computer
Assisted
Diagnosis
(CAD)
systems
[6].
Despite
this
,
image
processing
algor
ithms
are
not
perf
ect.
Some
of
the
most
utiliz
ed
algor
ithms
relied
upon
to
enhance
the
quality
of
chest
r
adiog
r
aphs
include
par
ameter
iz
ed
logar
ithmic
image
filter
ing
method
based
on
Laplacian
of
Gaussian
(LoG)
[7],
the
Hessian-LoG
filter
[8],
and
the
mean
and
median
filter
ing
f
or
noise
remo
v
al
[9].
These
filters
are
only
appropr
iate
f
or
cer
tain
types
of
noises
and
are
inadequate
f
or
enhancing
medical
images
such
as
a
chest
r
adiog
r
aph.
Recently
,
a
total
v
ar
iation
approach
has
been
used
to
enhance
the
local
contr
ast
in
chest
r
adiog
r
aphs
leading
to
significant
impro
v
ement.
[4].
Another
common
technique
used
in
f
eature-based
enhancement
classification
is
the
classic
unshar
p
masking
method.
The
Fully
Con
v
olutional
Neur
al
Netw
or
ks
(FCNN)
is
utiliz
ed
to
impro
v
e
the
contr
ast
of
delicate
lung
str
uctures
in
chest
r
adiog
r
aphs
[10-14].
These
methods
impro
v
e
the
image
contr
ast
b
ut
not
only
f
all
shor
t
in
detecting
lung
cancer
nodules
,
b
ut
in
addition,
lead
to
unacceptab
le
n
umber
of
f
alse
positiv
es
and
f
alse
negativ
es
.
W
e
implement
a
discrete
w
a
v
elet
tr
ansf
or
m
combined
with
mor
phological
tools
to
enhance
chest
r
adiog
r
aphs
.
This
leads
to
an
efficient
method
of
denoising
and
enhancement
Receiv
ed
December
4,
2018;
Re
vised
F
ebr
uar
y
8,
2019;
Accepted
March
12,
2019
Evaluation Warning : The document was created with Spire.PDF for Python.
TELK
OMNIKA
ISSN:
1693-6930
2588
of
x-r
a
y
images
,
that
outperf
or
ms
w
a
v
elet
tr
ansf
or
ms
based
techniques
[15].
Our
algor
ithm
is
implemented
in
Python,
leading
to
consistency
in
the
quality
of
the
results
obtained.
The
str
ategy
str
ikingly
enhances
points
of
interest
in
chest
r
adiog
r
aphs
while
preser
ving
t
he
details
of
delicate
chest
tissue
,
so
r
adiologists
ma
y
ha
v
e
a
more
e
xact
clar
ification
of
diagnosis
[16].
Due
to
its
m
ultiresolution
capabilities
,
the
w
a
v
elet
tr
ansf
or
m
has
become
a
po
w
erful
image
processing
tool
[17,
18].
W
a
v
elets
ha
v
e
a
localizing
proper
ty
and
a
char
acter
istic
of
denoising
in
a
time-scale
d
omain
and
hence
making
a
v
ailab
le
local
details
of
an
image
with
minimal
loss
of
detail.
W
a
v
elet
based
image
enhancement
techniques
such
as
histog
r
am
equalization
and
gamma
adjustments
when
applied
to
chest
r
adiog
r
aphs
,
ho
w
e
v
er
,
lead
to
loss
of
vital
image
details
[19].
Existing
methods
such
as
k
er
nel
and
spline
estimators
,
tend
not
to
resolv
e
local
str
uctures
w
ell,
and
spatial
techniques
such
as
the
median
and
mean
filters
ha
v
e
the
demer
it
of
b
lurr
ing
the
edges
of
an
image
in
an
attempt
to
smoothen
the
image
to
remo
v
e
noise
[17].
This
is
catastrophic
in
applications
to
medical
imaging.
In
order
to
eliminate
the
prob
lems
listed
abo
v
e
,
w
e
introduce
a
technique
to
remo
v
e
anatomical
noise
whiles
preser
ving
details
.
Firstly
,
w
e
use
the
dy
adic
w
a
v
elet
tr
ansf
or
m
that
has
the
capability
to
locally
decompose
an
image
to
remo
v
e
the
unw
anted
details
and
then
reconstr
uct
the
image
using
the
der
iv
ed
w
a
v
elet
coefficients
.
W
e
then
apply
the
mor
phological
erosion
and
dilation
f
or
se
v
er
al
iter
ations
to
enhance
the
cancer
nodules
and
realiz
e
a
better
appear
ance
,
using
a
small
and
ellipsoidal
str
uctur
ing
element.
2.
Morphological
Er
osion
and
Dilation
Mathematical
mor
phology
is
a
technique
f
or
e
xtr
acting
and
analysing
the
par
ts
of
an
image
that
are
of
interest
to
the
researcher
.
It
is
based
on
set
theoretical
axioms
and
is
der
iv
ed
from
the
basic
Mink
o
wski
set
oper
ations
of
addition
and
subtr
action.
In
this
f
or
m
ulation,
images
are
considered
as
functions
mapped
from
the
euclidean
space
M
into
R
[
f1
;
1g
.
Str
uctur
ing
functions
which
are
kno
wn
as
str
uctur
ing
elements
are
functions
of
the
same
f
or
m
as
the
images
.
Giv
en
tw
o
image
sets
A
and
B
,
the
mink
o
wski
addition
is
defined
b
y
A
B
=
[
2
B
(
A
+
)
and
the
subtr
action
is
defined
b
y
A
B
=
\
2
B
(
A
+
)
.
The
set
A
represents
the
image
data
and
the
set
B
is
the
str
uctur
ing
element.
The
str
uctur
ing
element
pla
ys
a
similar
role
that
con
v
olution
k
er
nels
pla
y
in
linear
image
filter
ing.
The
basic
mor
phological
o
per
ators
,
dilation
and
erosion
are
based
on
these
tw
o
oper
ations
.
Giv
en
an
image
function
f
(
x
)
and
a
str
uctur
ing
function
s
(
x
)
,
the
g
r
a
yscale
dilation
of
f
b
y
s
is
defined
as
D
(
f
;
s
)
=
(
f
s
)(
x
)
=
sup
y
2
M
[(
f
(
y
)
+
s
(
x
y
)]
and
erosion
of
f
b
y
s
is
defined
b
y
E
(
f
;
s
)
=
(
f
s
)(
x
)
=
inf
y
2
M
[(
f
(
y
)
s
(
y
x
)]
A
flat
str
uctur
ing
function
is
defined
as
s
(
x
)
=
(
0
;
x
2
S
1
other
w
ise
S
M
,
is
the
str
uctur
ing
function
suppor
t.
With
this
kind
of
str
uctur
ing
element,
only
tr
ue
pix
els
are
count
ed
in
the
mor
phological
computation.
This
thus
simplifies
the
definitions
of
dilation
and
erosion
to
D
(
f
;
s
)
=
(
f
s
)(
x
)
=
sup
z
2
S
s
(
f
(
x
+
z
)
and
E
(
f
;
s
)
=
(
f
s
)(
x
)
=
inf
z
2
S
(
f
(
x
+
z
)
Chest
r
adiog
r
aph
image
enhancement
with...
(Anthon
y
Aidoo)
Evaluation Warning : The document was created with Spire.PDF for Python.
2589
ISSN:
1693-6930
respectiv
ely
,
where
S
s
denotes
the
symmetr
ic
str
uctur
ing
function
suppor
t.
Applying
dilation
oper
ation
to
an
object
causes
it
to
g
ro
w
in
siz
e
b
y
the
str
uctur
ing
element,
whereas
erosion
causes
the
object
to
shr
ink
[20,
21,
22,
23].
Cancer
nodules
ha
v
e
high
intensity
v
alues
than
the
adjacent
usually
br
ight
anatomical
str
uctures
[24].
As
such,
w
e
erode
the
image
first
to
get
r
id
of
all
noisy
details
and
then
w
e
dilate
the
result
using
an
elliptical
str
uctur
ing
element.
This
significantly
imp
ro
v
es
the
visibility
of
nodules
to
e
v
en
the
nak
ed
e
y
e
in
X-Ra
ys
.
3.
Results
W
e
implemented
our
technique
on
a
database
of
a
set
of
247
chest
X-r
a
y
images
from
a
standard
Pub
lic
Database;
the
J
apanese
Society
of
Radiological
T
echnology
.
This
database
is
endo
w
ed
with
diff
erent
cases
which
mak
es
it
the
appropr
iate
choice
.
These
images
w
ere
collected
o
v
er
a
three
y
ear
per
iod
from
14
medical
institutions
and
are
made
up
of
anter
ior
and
poster
ior
films
of
measure
14
14
inches
.
There
are
154
images
which
ha
v
e
lung
nodules
,
out
of
which
100
are
malignant
and
54
are
benign.
93
of
the
images
are
without
lung
nodules
.
Nodules
are
confir
med
b
y
CT
and
their
locations
are
confir
med
b
y
three
r
adiologists
[25].
W
e
im
plemented
our
technique
on
100
images
with
nodules
and
60
images
with
no
nodules
.
Our
result
sho
w
ed
visib
le
presence
of
nodules
in
100
%
of
the
images
with
nodules
,
eliminating
completely
the
occurence
of
f
alse
positiv
es
and
f
alse
negativ
es
.
Samples
of
our
results
are
displa
y
ed
in
the
Figures
1.
Figure
1
is
a
w
a
v
elet
decomposition
and
reconstr
uction
compared
with
the
or
iginal
images
.
Figure
1.
(a-c)
are
the
or
iginal
chest
images
with
nodules
,
(d-f)
are
the
w
a
v
elet
decomposition
of
images
in
(a-c),
(g-i)
are
the
reconstr
ucted
images
from
the
decomposed
images
(d-f)
Figure
2
f
or
ms
par
t
of
the
sample
used
in
Figure
1.
It
sho
ws
the
w
a
v
elet
decomposition
and
reconstr
uction
compared
with
the
or
iginal
images
.
Figure
3
sho
ws
a
w
a
v
elet
decomposition
and
reconstr
uction
compared
with
the
or
iginal
images
.
Figure
4
completes
the
set
of
images
that
compare
w
a
v
elet
decomposition
and
reconstr
uction
with
the
or
iginal
images
.
Mor
phological
erosion
and
dilation
are
successiv
ely
applied
to
the
sample
of
chest
r
adiog
r
aph
images
.
P
ar
t
of
the
results
are
sho
wn
in
Figure
5.
Figure
6
is
the
second
sample
of
images
that
ha
v
e
been
processed
with
mor
phological
oper
ations
.
The
third
set
of
the
results
of
processing
the
sample
TELK
OMNIKA
V
ol.
17,
No
.
5,
October
2019
:
2587
2594
Evaluation Warning : The document was created with Spire.PDF for Python.
TELK
OMNIKA
ISSN:
1693-6930
2590
of
images
with
mor
phological
erosion
and
dilation
is
displa
y
ed
in
Figure
7.
The
final
set
of
the
results
of
processing
the
sample
of
images
with
mo
r
phological
erosion
and
dilation
is
displa
y
ed
in
Figure
8.
Figure
2.
This
is
par
t
of
the
sample
of
images
sho
wn
in
Figure
1.
(a-b)
are
the
or
iginal
chest
images
with
nodules
,
(c-d)
are
the
w
a
v
elet
decomposition
of
images
in
(a-b),
(e-f)
are
the
reconstr
ucted
images
from
the
decomposed
images
(c-d)
Figure
3.
(a-c)
are
the
or
iginal
chest
images
without
nodules
,
(d-f)
are
the
w
a
v
elet
decomposition
of
images
in
(a-c),
(g-i)
are
the
reconstr
ucted
images
from
the
decomposed
images
(d-f)
Chest
r
adiog
r
aph
image
enhancement
with...
(Anthon
y
Aidoo)
Evaluation Warning : The document was created with Spire.PDF for Python.
2591
ISSN:
1693-6930
Figure
4.
This
is
par
t
of
the
sample
of
images
in
Figure
3
which
sho
ws
(a-b)
are
the
or
iginal
chest
images
without
nodules
,
(c-d)
are
the
w
a
v
elet
decomposition
of
images
in
(a-b),
(e-f)
are
the
reconstr
ucted
images
from
the
decomposed
images
(c-d)
Figure
5.
(a-c)
are
the
result
of
applying
mor
phological
erosion
to
reconstr
ucted
chest
images
with
nodules
,
(d-f)
sho
w
dilation
of
the
eroded
images
in
(a-c)
(Opening),
(g-i)
sho
w
in
v
er
ted
images
of
the
images
in
(d-f)
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.
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TELK
OMNIKA
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2592
Figure
6.
This
is
par
t
of
the
sample
of
images
sho
wn
in
Figure
5.
(a-b)
are
the
result
of
applying
mor
phological
erosion
to
reconstr
ucted
chest
images
with
nodules
,
(c-d)
sho
w
dilation
of
the
eroded
images
in
(a-b),
(e-f)
sho
w
in
v
er
ted
images
of
the
images
in
(c-d)
Figure
7.
Mor
phological
erosion
applied
to
reconstr
ucted
chest
images
without
nodules
,
(a-c)
are
the
result
of
applying
mor
phological
erosion
to
reconstr
ucted
chest
images
with
nodules
,
(d-f)
sho
w
dilation
of
the
eroded
images
in
(a-c),
(g-i)
sho
w
in
v
er
ted
images
of
the
images
in
(d-f)
Chest
r
adiog
r
aph
image
enhancement
with...
(Anthon
y
Aidoo)
Evaluation Warning : The document was created with Spire.PDF for Python.
2593
ISSN:
1693-6930
Figure
8.
This
is
par
t
of
the
sample
of
images
sho
wn
in
Figure
7.
Mor
phological
erosion
applied
to
reconstr
ucted
chest
images
without
nodules
,(a-b)
are
the
result
of
applying
mor
phological
erosion
to
reconstr
ucted
chest
images
without
nodules
,
(c-d)
sho
w
dilation
of
the
eroded
images
in
(a-b),
(e-f)
sho
w
in
v
er
ted
images
of
the
images
in
(c-d)
4.
Discussion
and
Conc
lusion
W
e
de
v
eloped
a
technique
f
or
im
age
denoising
and
enhancement
based
on
a
combination
of
w
a
v
elets
and
mor
phological
erosion
and
dilation
which
is
presented
and
applied
to
a
large
sample
of
chest
x-r
a
y
images
,
some
of
which
contained
cancer
nodules
in
order
to
enhance
the
quality
and
contr
ast
of
the
x-r
a
y
images
.
Our
approach
is
tested
on
a
n
umber
of
pub
licly
a
v
ailab
le
chest
r
adiog
r
aph
images
.
The
combined
w
a
v
elet
based
and
mathematical
mor
phology
technique
retains
and
elucidate
more
detail
image
inf
or
mation
o
f
interest
on
both
cancer
nodules
and
anatomical
str
uctures
captured
in
chest
r
adiog
r
aphs
.
The
technique
not
only
suppresses
unw
anted
noise
,
it
also
preser
v
es
the
edges
of
the
nodules
to
enab
le
accur
ate
detection.
F
rom
the
results
obtained,
w
e
conclude
that
our
technique
is
efficient
and
compares
f
a
v
or
ab
ly
with
nonmor
phological
based
techniques
f
or
chest
r
adiog
r
aph
image
enhancement.
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OMNIKA
ISSN:
1693-6930
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(Anthon
y
Aidoo)
Evaluation Warning : The document was created with Spire.PDF for Python.