T
E
L
KO
M
N
I
KA
T
e
lec
om
m
u
n
icat
ion
,
Com
p
u
t
i
n
g,
E
lec
t
r
on
ics
an
d
Cont
r
ol
Vol.
18
,
No.
1
,
F
e
br
ua
r
y
2020
,
pp.
148
~
155
I
S
S
N:
1693
-
6930,
a
c
c
r
e
dit
e
d
F
ir
s
t
G
r
a
de
by
Ke
me
nr
is
tekdikti
,
De
c
r
e
e
No:
21/E
/KP
T
/2018
DO
I
:
10.
12928/
T
E
L
KO
M
NI
KA
.
v18i1.
14462
148
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omepage
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tp:
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s
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i
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eral
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h
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e
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earch
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et
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s
y
s
t
em
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o
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s
i
s
.
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h
i
s
a
s
s
i
s
t
an
ce
s
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s
t
em
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as
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ect
i
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accu
rac
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f
7
6
%
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m
p
ared
t
o
d
o
ct
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r's
j
u
s
t
i
f
i
ca
t
i
o
n
.
K
e
y
w
o
r
d
s
:
B
lende
d
me
thod
B
one
de
ns
it
y
Os
teopor
os
is
Ve
r
tebr
a
Th
i
s
i
s
a
n
o
p
en
a
c
ces
s
a
r
t
i
c
l
e
u
n
d
e
r
t
h
e
CC
B
Y
-
SA
l
i
ce
n
s
e
.
C
or
r
e
s
pon
din
g
A
u
th
or
:
S
is
wo
W
a
r
doyo,
P
os
tgr
a
dua
te
P
r
ogr
a
m
in
T
e
c
h
nology
a
nd
Voc
a
ti
o
n
a
l
E
duc
a
ti
on,
Yogya
ka
r
ta
S
tate
Unive
r
s
it
y,
I
ndone
s
ia
.
E
mail:
s
is
wo@
unti
r
ta.
a
c
.
id
1.
I
NT
RODU
C
T
I
ON
Os
teopor
os
is
is
a
c
omm
on
dis
e
a
s
e
c
ha
r
a
c
ter
is
e
d
by
a
s
ys
temic
im
pa
ir
ment
of
bone
mas
s
a
nd
mi
c
r
oa
r
c
hit
e
c
tur
e
that
r
e
s
ult
s
in
f
r
a
gil
i
ty
f
r
a
c
tur
e
s
[
1,
2]
.
B
one
is
the
mos
t
im
por
tant
e
leme
nt
in
th
e
human
body
a
s
a
mate
r
ial
of
body
f
o
r
mi
ng
a
nd
s
tr
e
n
gthening.
I
n
book
[
3]
a
nd
a
r
ti
c
le
[
4]
,
bone
q
ua
li
ty
is
a
manif
e
s
tation
o
f
a
r
c
hit
e
c
tur
e
(
bone
ge
ometr
y
,
mi
c
r
o
a
r
c
hit
e
c
tur
e
,
c
or
t
ica
l
thi
c
kne
s
s
,
a
nd
tr
a
be
c
ull
a
r
c
onne
c
ti
vit
y)
,
matr
ix
,
a
nd
mi
ne
r
a
li
z
a
ti
on.
M
e
a
s
ur
e
ment
B
M
D
is
the
pr
incipa
l
method
of
diag
nos
is
of
os
teopor
os
is
be
c
a
u
s
e
pa
ti
e
nts
with
low
B
M
D
va
l
ue
s
ha
ve
e
leva
te
d
r
is
k
of
de
ve
lopi
ng
a
bone
f
r
a
c
tur
e
[
5]
.
Os
teopor
os
is
is
c
h
a
r
a
c
ter
ize
d
by
a
n
a
bs
olut
e
de
c
r
e
a
s
e
in
the
a
mount
of
bone
to
a
leve
l
be
low
that
r
e
q
uir
e
d
f
or
mec
ha
nica
l
s
uppor
t
of
nor
mal
a
c
ti
vi
ty
a
nd
by
t
he
oc
c
ur
r
e
nc
e
of
non
-
tr
a
umatic
s
ke
leta
l
f
r
a
c
tur
e
[
6,
7]
.
P
ha
r
mac
ologi
c
a
ge
nts
that
in
f
luenc
e
bone
r
e
modeling
a
r
e
a
n
e
s
s
e
nti
a
l
c
omponent
of
os
teopor
os
is
mana
ge
ment.
B
e
c
a
us
e
many
pa
ti
e
nts
a
r
e
f
i
r
s
t
dia
gnos
e
d
with
os
teopor
os
is
whe
n
p
r
e
s
e
nti
ng
with
a
f
r
a
gil
it
y
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
V
e
r
tebr
a
os
teopor
o
s
is
de
tec
ti
on
bas
e
d
on
bone
de
ns
it
y
us
ing
I
nde
x
-
Singh
…
(
Sis
w
o
W
ar
doy
o
)
149
f
r
a
c
tur
e
,
it
is
c
r
it
ica
l
to
unde
r
s
tand
how
os
teopor
ot
ic
medic
a
ti
ons
inf
luenc
e
f
r
a
c
tur
e
he
a
li
ng
[
8
]
.
T
his
s
it
ua
ti
on
is
h
igh
r
is
k
be
c
a
us
e
the
bone
s
be
c
ome
f
r
a
gil
e
a
nd
e
a
s
il
y
c
r
a
c
ke
d
a
nd
e
ve
n
br
oke
n.
M
a
ny
pe
ople
do
not
r
e
a
li
z
e
that
os
teopor
os
is
is
a
s
il
e
nt
dis
e
a
s
e
s
.
Ac
c
or
ding
to
the
wor
ld
he
a
lt
h
or
ga
niza
ti
on
(
W
HO
)
s
tate
s
to
da
te
a
bout
200
mi
ll
ion
pe
ople
s
uf
f
e
r
f
r
o
m
os
teopor
os
i
s
a
nd
the
r
e
s
t
of
the
wor
ld
a
nd
will
mul
ti
ply
in
n
umber
in
s
ubs
e
que
nt
ye
a
r
s
.
Ac
c
or
ding
to
the
I
ndone
s
ian
M
ini
s
tr
y
o
f
He
a
lt
h
(
2015)
[
9
]
,
the
im
pa
c
t
o
f
os
teopor
os
is
in
I
ndone
s
ia
in
2006
wa
s
a
l
r
e
a
dy
a
t
a
n
a
lar
mi
ng
r
a
te
,
r
e
a
c
hing
19.
7
%
of
the
population
a
nd
c
onti
nuing
to
incr
e
a
s
e
with
the
incr
e
a
s
ing
number
o
f
e
lder
ly
pe
ople
in
the
f
oll
owing
ye
a
r
s
.
T
he
M
ini
s
tr
y
o
f
He
a
lt
h
s
tate
s
that
two
out
o
f
f
ive
pe
ople
in
I
ndone
s
ia
a
r
e
a
t
r
is
k
of
de
ve
lopi
n
g
os
t
e
opor
os
is
.
I
nter
na
ti
ona
l
os
teopor
os
is
f
ounda
t
ion
a
ls
o
s
tate
s
th
a
t
one
in
thr
e
e
wome
n
a
nd
one
in
f
ive
men
a
r
e
a
t
r
is
k
f
or
os
teopor
os
is
.
I
n
I
ndone
s
ian
wome
n,
the
r
is
k
of
os
teopor
os
is
is
23%
a
t
50
-
80
ye
a
r
s
a
nd
will
in
c
r
e
a
s
e
to
53%
by
the
a
ge
o
f
70
-
80
ye
a
r
s
,
t
his
da
t
a
s
howe
d
high
leve
l
whe
n
c
ompar
e
d
to
other
As
ian
c
ountr
i
es
[
10]
.
Although
c
ha
nge
s
in
the
bone
mas
s
a
nd
c
a
lcium
meta
boli
s
m
a
r
e
e
vident
in
the
pr
e
menopa
us
a
l
pe
r
iod,
menopa
us
e
mar
ks
the
be
ginni
ng
of
a
bone
los
s
that
c
onti
nue
s
unti
l
the
e
nd
of
li
f
e
a
nd
is
the
main
c
a
us
e
of
os
teopor
oti
c
f
r
a
c
tur
e
s
in
e
lder
ly
wom
e
n
[1
1]
.
Oe
s
tr
oge
n
tr
e
a
tm
e
nt
im
pr
ove
d
the
c
a
lcium
ba
lanc
e
in
thes
e
wome
n
a
nd
a
r
r
e
s
ted
th
e
ir
he
ight
los
s
.
T
he
s
e
fi
ndings
we
r
e
the
ba
s
is
f
or
de
fi
ning
pos
tm
e
n
opa
us
a
l
os
teopor
os
i
s
a
nd
e
s
t
a
bli
s
he
d
the
li
nk
be
twe
e
n
os
teopor
os
is
a
nd
ve
r
tebr
a
l
f
r
a
c
tu
r
e
s
.
S
e
ve
r
a
l
s
ub
s
e
que
nt
s
tudi
e
s
ha
ve
de
mons
tr
a
ted
that
ve
r
tebr
a
f
r
a
c
tur
e
s
r
e
pr
e
s
e
nt
im
pa
ir
e
d
bone
qua
li
ty
a
nd
s
tr
uc
tu
r
a
l
de
c
a
y
of
the
bone
.
Ve
r
tebr
a
f
r
a
c
tur
e
s
r
e
fl
e
c
t
the
s
e
ve
r
it
y
of
os
teopor
os
is
a
nd
a
r
e
s
tr
ong
pr
e
d
ictor
s
of
f
utur
e
f
r
a
c
tur
e
s
,
thus
s
e
r
ving
a
s
the
ha
ll
mar
k
of
the
d
is
e
a
s
e
[
12]
.
I
mpr
ove
d
he
a
lt
hc
a
r
e
,
s
oc
io
-
e
c
onomi
c
a
nd
li
f
e
s
t
yle
c
ha
nge
s
ha
ve
led
to
a
d
r
a
matica
ll
y
incr
e
a
s
e
d
li
f
e
e
xpe
c
tanc
y
in
moder
n
s
oc
ieties
dur
ing
the
pa
s
t
c
e
ntur
y
[
12]
.
T
o
s
uppr
e
s
s
the
g
r
owth
r
a
te
of
os
teopor
os
is
s
uf
f
e
r
e
r
s
,
we
ne
e
d
e
a
r
ly
inf
o
r
mation
a
bout
the
c
ondit
ion
of
bone
de
ns
it
y
that
we
ha
ve
.
T
his
will
pr
ovide
a
f
a
s
ter
r
e
s
pons
e
to
us
to
maintain
our
diet
a
nd
a
t
t
he
s
a
me
ti
me
incr
e
a
s
e
the
int
a
ke
of
bone
nutr
it
ion
that
our
body
ne
e
ds
s
o
that
our
bone
s
do
not
be
c
ome
br
it
tl
e
quickly.
T
he
e
a
r
ly
diagn
os
is
of
os
teopor
os
is
is
c
r
uc
ial
to
mi
ti
ga
te
the
s
oc
ial
a
nd
e
c
onomi
c
bu
r
de
n
due
to
e
n
s
uing
os
teopor
oti
c
f
r
a
c
tur
e
s
[
13
,
14
]
.
I
nit
ial
in
f
or
mation
on
os
teopor
os
is
c
a
n
be
known
thr
ough
bone
de
ns
it
y,
but
bone
mi
ne
r
a
l
de
ns
it
y
doe
s
not
e
xplain
e
ve
r
ythi
ng
[
15]
.
B
on
e
de
ns
it
ies
o
f
a
ll
r
e
gions
o
f
the
h
ip
we
r
e
s
tr
ong
ly
r
e
late
d
to
th
e
r
is
k
of
hip
f
r
a
c
tur
e
[
14]
.
How
e
ve
r
,
s
a
mpl
e
s
to
make
the
os
teopor
os
is
diagnos
i
s
a
id
s
y
s
tem
e
a
s
y
to
obtain.
I
n
a
ddit
ion
to
bone
de
ns
it
ometr
y
,
diagnos
ti
c
tool
s
that
a
r
e
ba
s
e
d
on
os
teopor
oti
c
f
r
a
c
tur
e
r
is
k
f
a
c
tor
s
s
uc
h
a
s
F
R
AX
a
r
e
inexpe
ns
ive
a
nd
e
a
s
y
mea
ns
to
identif
y
a
lar
ge
p
or
ti
on
of
the
population
f
o
r
pr
ope
r
tr
e
a
tm
e
nt.
R
e
f
e
r
r
ing
to
the
de
s
c
r
ipt
ion,
mea
s
ur
e
ment
of
bone
mi
ne
r
a
l
de
ns
it
y
(
B
M
D)
by
DE
XA
s
c
a
nning
is
the
ba
s
is
f
or
the
mana
ge
ment
o
f
os
teopor
os
is
[
13,
16]
.
B
e
f
or
e
t
he
1994
wor
ld
he
a
lt
h
or
ga
niza
ti
on
(
W
HO
)
c
las
s
if
i
c
a
ti
on
of
“
nor
mal”
ve
r
s
us
“
os
teope
nia”
ve
r
s
u
s
“
os
teopo
r
os
is
”
ba
s
e
d
on
bone
mi
ne
r
a
l
de
ns
it
y
(
B
M
D)
va
lues
(T
-
s
c
or
e
s
)
,
the
diagnos
is
of
os
teopor
os
is
s
ti
ll
w
a
s
made
on
the
ba
s
is
of
low
-
tr
a
uma
f
r
a
c
tur
e
s
[
17,
18]
.
I
n
a
nother
s
tudy
[
6]
,
the
S
ingh’
s
index
(
S
I
)
is
a
n
inexpe
ns
ive
s
im
ple
method
of
a
s
s
e
s
s
ing
bone
de
ns
it
y
a
t
a
s
it
e
whe
r
e
f
r
a
c
tur
e
s
oc
c
ur
.
T
he
S
I
ha
s
be
e
n
c
r
i
ti
c
ize
d
f
or
it
s
low
r
e
li
a
bil
it
y
due
to
the
s
ubjec
ti
ve
na
tur
e
of
i
ts
will
de
f
ined
g
r
a
ding
f
o
r
os
teopor
os
is
.
B
a
s
e
d
on
the
de
s
c
r
ipt
ion
a
bove
,
thi
s
r
e
s
e
a
r
c
h
us
ing
blende
d
s
ingh
index
with
s
tatis
ti
c
a
l
methods
.
T
his
is
to
im
p
r
ove
the
r
e
li
a
bil
it
y
o
f
os
teopor
os
is
-
a
s
s
is
ted
diangnos
i
s
s
ys
tem
s
us
ing
X
-
r
a
y
s
c
a
nnin
g.
E
a
r
ly
diagnos
is
of
os
teopor
os
is
us
e
s
qua
nti
f
ica
ti
on
of
digi
tal
x
-
r
a
y
da
ta
.
T
his
r
e
s
e
a
r
c
h
us
e
s
im
a
ge
pr
oc
e
s
s
ing
a
lgor
it
hms
a
c
c
or
ding
to
publi
c
a
ti
on
[
19,
20]
,
ha
t
c
ould
ge
ne
r
a
te
s
ha
r
pe
r
a
nd
c
lea
r
e
r
im
a
ge
s
or
im
pr
ove
the
qua
li
ty
of
inf
or
mation
c
ontaine
d
in
the
im
a
g
e
s
o
tha
t
it
c
a
n
be
v
is
ua
ll
y
int
e
r
pr
e
ted
be
tt
e
r
,
i.
e
im
a
ge
e
nha
nc
e
ment,
two
-
dim
e
ns
ional
c
onvolut
ion,
a
li
gnment
a
nd
s
ha
r
pe
ning
of
im
a
ge
s
,
a
nd
e
dge
de
tec
ti
on.
S
tate
of
the
a
r
t
thi
s
r
e
s
e
a
r
c
h
is
us
ing
blende
d
method
be
twe
e
n
s
tatis
ti
c
a
l
with
I
nde
x
-
S
ingh
f
r
om
ve
r
te
br
a
b
one
de
ns
it
y.
T
his
a
uxil
iar
y
s
ys
tem
is
c
he
a
p,
e
a
s
y
a
nd
h
igh
a
c
c
ur
a
c
y.
2.
RE
S
E
AR
CH
M
E
T
HO
D
I
n
thi
s
r
e
s
e
a
r
c
h,
the
input
im
a
ge
is
a
x
-
r
a
y
os
t
e
opor
os
is
im
a
ge
of
the
s
pinal
ve
r
tebr
a
pa
ti
e
nts
obtaine
d
f
r
om
x
-
r
a
y
plane
de
vice
unde
r
the
br
a
nd
na
me
S
him
a
dz
u
R
AD
S
P
E
E
D.
T
ype
c
a
ptur
e
d
i
mage
in
the
s
pinal
c
or
d
(
s
pinal
ve
r
teb
r
a
)
pr
oduc
e
s
i
mage
f
il
e
s
in
the
f
o
r
mat
o
f
the
jo
int
photog
r
a
phic
gr
ou
p
(
J
P
G)
with
a
r
e
s
olut
ion
of
624x762
pixels
.
X
-
r
a
y
im
a
ge
s
us
e
d
f
r
om
the
S
oe
ha
r
s
o
Or
thopedic
Hos
pit
a
l
in
S
ur
a
ka
r
ta
ha
ve
be
e
n
va
li
da
ted
by
s
pe
c
ialis
t
doc
tor
s
.
X
-
r
a
ys
im
a
ge
we
r
e
obtaine
d
a
s
many
a
s
50
s
a
m
ples
e
a
c
h
with
jus
ti
f
ica
ti
on
of
s
pe
c
ialis
t
doc
tor
s
,
i.
e
,
os
teopor
os
is
,
os
teope
nia,
a
nd
nor
mal
.
F
lowc
ha
r
t
o
f
the
r
e
s
e
a
r
c
h
pr
oc
e
s
s
s
hown
in
F
igur
e
1.
T
he
f
ir
s
t
s
tage
in
thi
s
r
e
s
e
a
r
c
h
is
c
r
op
ping
im
a
ge
.
M
e
thods
a
nd
s
ys
tems
a
r
e
pr
o
vided
f
or
c
r
opping
a
d
igi
tal
i
mage
ba
s
e
d
on
moveme
nt
da
ta
[
21]
.
T
his
c
r
opping
pr
oc
e
s
s
is
c
a
r
r
ied
out
on
x
-
r
a
y
im
a
ge
s
with
a
r
e
s
olut
ion
of
624x762
pixels
to
126x234
pixels
.
C
r
opping
is
done
on
the
r
e
gion
of
in
ter
e
s
t
(
R
OI
)
im
a
ge
of
the
bone
a
lone
by
t
r
im
mi
ng
the
e
dge
s
of
the
x
-
r
a
y
im
a
ge
.
C
r
opping
is
done
to
c
lar
i
f
y
t
he
s
pinal
ve
r
tebr
a
im
a
ge
objec
t
that
will
be
pr
oc
e
s
s
e
d
i.
e
,
only
f
oc
us
e
s
on
the
s
pe
c
if
ied
objec
t.
T
his
is
to
s
im
pli
f
y
a
na
lys
is
a
nd
s
tor
a
ge
s
ize
o
f
the
im
a
ge
.
T
he
ne
xt
pr
oc
e
s
s
is
to
r
e
s
ize
the
im
a
ge
o
f
the
c
r
op.
T
h
is
p
r
oc
e
s
s
is
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
1693
-
6930
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
,
Vol.
18
,
No
.
1
,
F
e
br
ua
r
y
2020
:
148
-
155
150
c
a
r
r
ied
out
to
c
ha
nge
the
x
-
r
a
y
im
a
ge
r
e
s
olut
ion
to
127x127
pixels
,
s
o
that
the
di
mens
ions
of
the
x
-
r
a
y
im
a
ge
a
r
e
the
s
a
me
be
twe
e
n
ho
r
izonta
l
a
nd
ve
r
ti
c
a
l.
I
n
p
ubli
c
a
ti
on
[
22]
,
c
ha
nging
im
a
g
e
s
ize
is
int
e
nde
d
t
o
r
e
duc
e
the
wor
kload
of
the
c
omput
e
r
s
o
the
c
ompu
tation
c
a
n
be
done
mor
e
qui
c
kly.
T
he
ne
xt
s
tage
is
a
pr
e
li
mi
na
r
y
pr
oc
e
s
s
(
pr
e
-
pr
oc
e
s
s
ing)
.
T
he
pe
r
f
or
med
to
obtain
a
good
im
a
ge
qua
li
ty
[
23]
a
s
we
ll
a
s
obtain
the
r
e
s
ult
s
of
white
a
nd
b
lac
k
pixels
f
r
o
m
x
-
r
a
y
im
a
ge
s
of
the
s
pinal
ve
r
tebr
a
.
T
his
s
e
c
ti
on
is
done
s
o
that
the
de
s
ir
e
d
ob
jec
t
c
a
n
be
obtaine
d
with
maximum
r
e
s
ult
s
.
T
he
r
e
s
ize
d
x
-
r
a
y
im
a
ge
da
ta
is
then
c
onve
r
ted
int
o
a
gr
a
y
im
a
ge
.
Af
ter
going
thr
ough
the
gr
a
ys
c
a
le
pr
e
-
pr
oc
e
s
s
ing,
the
x
-
r
a
y
im
a
ge
goe
s
int
o
the
ne
xt
s
tage
by
s
e
tt
ing
the
thr
e
s
holdi
ng
va
lue
in
or
de
r
to
de
ter
mi
ne
the
blac
k
a
n
d
white
leve
l
in
the
i
mage
,
thi
s
s
tage
is
c
a
ll
e
d
binar
y
im
a
g
e
.
T
he
pu
r
pos
e
of
thi
s
p
r
oc
e
s
s
is
f
ind
out
the
obje
c
t
r
e
s
ult
s
f
r
om
the
number
of
pixels
x
-
r
a
y
im
a
ge
o
f
the
s
pinal
bone
ve
r
tebr
a
that
e
xis
ts
on
a
wide
a
r
e
a
.
P
ubli
c
a
ti
on
[
22
]
s
hows
the
e
qua
ti
on
us
e
d
to
de
ter
mi
ne
the
a
r
e
a
that
is
c
a
lcula
ted
us
ing
the
f
oll
owing
e
qua
ti
on:
=
∑
∑
(
,
)
=
1
=
1
(
1)
whe
r
e
n
is
r
ow
,
m
is
c
olu
mn,
f
(
i
,
j
)
=
1
if
(
i
,
j
)
is
a
n
objec
t
pixel.
Af
ter
c
a
lcula
ti
ng
the
a
r
e
a
,
then
pr
oc
e
e
d
to
c
a
lc
ulate
bone
de
ns
it
y.
B
one
mi
ne
r
a
l
de
ns
it
y
is
a
n
im
por
tant
pa
r
a
mete
r
to
ind
ica
te
the
c
ondit
ion
of
t
he
bone
on
a
ny
s
it
e
o
f
the
body
[
24]
.
T
h
is
s
tage
is
us
e
d
to
de
ter
mi
ne
the
de
ns
it
y
of
bone
x
-
r
a
y
im
a
ge
s
.
B
one
de
ns
it
y
is
c
a
lcula
ted
by
mul
ti
plyi
ng
pixel
c
olum
ns
a
nd
r
ows
of
s
pinal
v
e
r
tebr
a
x
-
r
a
y
i
mage
s
.
T
he
r
e
s
ult
s
of
the
manua
l
c
a
lcula
ti
on
,
mus
t
be
the
s
a
me
a
s
th
e
s
um
of
white
a
nd
blac
k
pixels
f
r
o
m
the
r
e
s
ult
s
of
the
pr
o
c
e
s
s
of
c
a
lcula
ti
ng
the
a
r
e
a
.
T
his
pixel
c
a
lcula
ti
on
will
be
a
r
e
f
e
r
e
nc
e
input
da
ta
int
o
the
ne
xt
method
.
M
a
nua
l
c
a
lcula
ti
on
o
f
bone
de
ns
it
y
a
nd
tot
a
l
pixel
a
r
e
a
us
ing
the
f
oll
owing
(
2)
a
nd
(
3
)
:
=
(
2
)
whe
r
e
T
p
is
tot
a
l
pixel
,
n
is
r
ow
a
nd
m
is
c
olumn.
=
∑
+
∑
(
3
)
whe
r
e
T
p
is
tot
a
l
pixel
,
i
is
white
pixel
a
nd
j
is
bla
c
k
pixe
l.
X
-
R
a
y
I
m
a
g
e
C
r
o
p
p
i
n
g
I
m
a
g
e
R
e
s
i
z
e
I
m
a
g
e
s
1
2
6
x
2
3
4
p
i
x
e
l
P
r
e
-
P
r
o
c
e
s
s
i
n
g
S
t
a
t
i
s
t
i
c
a
l
I
n
d
e
x
-
S
i
n
g
h
B
o
n
e
D
e
n
s
i
t
y
V
a
l
i
d
a
t
i
o
n
a
n
d
C
o
m
p
a
r
i
s
o
n
C
l
a
s
s
i
f
i
c
a
t
i
o
n
o
f
O
s
t
e
o
p
o
r
o
s
i
s
F
igur
e
1.
F
lowc
ha
r
t
of
the
r
e
s
e
a
r
c
h
pr
oc
e
s
s
T
he
ne
xt
s
tage
is
a
s
tatis
ti
c
a
l
c
a
lcula
ti
on
f
or
I
nde
x
-
S
ingh
c
las
s
if
ica
ti
on.
T
he
S
ingh
index
is
a
s
im
ple,
c
he
a
p
method
f
or
givi
ng
a
r
ough
mea
s
ur
e
ment
of
bone
mas
s
that
may
s
ti
ll
be
us
e
d
to
c
las
s
if
y
os
teopor
os
is
gr
a
de
in
c
ountr
ies
whe
r
e
a
c
c
e
s
s
to
dua
l
e
ne
r
gy
x
-
r
a
y
a
bs
or
pti
om
e
t
r
y
(
DE
XA
)
s
c
a
nning
is
li
mi
ted
[
25]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
V
e
r
tebr
a
os
teopor
o
s
is
de
tec
ti
on
bas
e
d
on
bone
de
ns
it
y
us
ing
I
nde
x
-
Singh
…
(
Sis
w
o
W
ar
doy
o
)
151
Although
S
ingh’
s
index
ha
s
a
low
s
e
ns
it
ivi
ty,
it
is
s
pe
c
ifi
c
in
diagnos
ing
low
bone
mas
s
,
s
ugge
s
ti
ng
it
s
s
uit
a
bil
it
y
f
or
us
e
in
c
las
s
if
ying
la
r
ge
populations
,
but
not
indi
viduals
with
os
teopor
os
is
[
26]
.
T
his
method
is
th
e
s
tage
of
c
oll
e
c
ti
ng
qua
nti
tative
da
ta
r
e
s
ult
s
f
r
om
the
c
a
lcula
ti
on
of
bone
de
ns
it
y
on
the
x
-
r
a
y
im
a
ge
of
the
s
pinal
ve
r
tebr
a
.
I
n
th
is
method
the
r
e
s
ult
of
the
a
mount
o
f
e
xis
ti
ng
da
ta
s
or
ted
one
by
one
int
o
a
t
a
ble
that
only
c
ons
is
ts
of
the
number
o
f
whi
te
pixels
on
ly
f
r
om
the
lar
ge
s
t
to
the
s
malles
t.
I
nde
x
-
S
ingh
ha
s
lev
e
ls
f
r
om
1
-
6.
T
his
method
is
us
e
d
a
s
a
gr
ouping
of
e
a
c
h
pi
xe
l
r
e
s
ult
int
o
g
r
a
de
or
bone
c
las
s
if
ica
ti
on
f
r
om
n
or
mal
to
os
teopor
os
is
.
F
r
om
the
method
that
ha
s
be
e
n
don
e
is
to
c
ombi
ne
be
twe
e
n
S
tatis
ti
c
a
l
a
nd
I
nde
x
-
S
in
gh,
then
blende
d
S
tatis
ti
c
a
l
I
nde
x
-
S
ingh
a
im
s
to
incr
e
a
s
e
th
e
va
lue
of
a
c
c
ur
a
c
y.
Gr
ouping
a
c
c
or
ding
to
I
nde
x
-
S
ingh
of
e
a
c
h
pixel
f
r
om
nor
mal
leve
l
bone
c
las
s
if
ica
ti
on
to
os
teopor
os
is
us
ing
the
f
oll
owing
e
qua
ti
on:
=
∑
∑
(
4
)
P
wp
=
W
p
W
p
+
B
p
×
100%
(
5
)
whe
r
e
P
wp
is
pe
r
c
e
ntage
of
white
pixels
,
W
p
is
white
pixe
ls
da
n
B
p
is
blac
k
pixels
.
3.
RE
S
UL
T
S
AN
D
AN
AL
YSI
S
T
he
f
ir
s
t
s
tage
in
thi
s
r
e
s
e
a
r
c
h
is
c
r
opping
im
a
ge
a
s
s
hown
in
F
igur
e
s
2
(
a
)
a
nd
2
(b
)
.
F
igur
e
2
(
a
)
is
a
n
or
igi
na
l
im
a
ge
f
r
om
S
oe
ha
r
s
o
O
r
thopedic
H
os
pit
a
l
in
S
ur
a
ka
r
ta
a
nd
F
igur
e
2
(
b
)
is
r
e
s
ult
c
r
opping.
T
he
or
igi
na
l
x
-
r
a
y
im
a
ge
with
a
r
e
s
olut
ion
of
624
x
762
pixels
,
while
the
r
e
s
ult
s
of
it
s
c
r
op
ping
a
r
e
126x234
pixels
.
Af
ter
that
the
r
e
s
izing
of
the
c
r
o
pping
im
a
ge
r
e
s
umes
.
R
e
s
ult
s
a
r
e
s
hown
in
F
igu
r
e
3
(
a
).
Af
ter
obtaining
the
r
e
s
ult
s
,
c
onti
nue
the
c
onve
r
s
i
on
of
x
-
r
a
y
im
a
ge
s
to
gr
a
y
im
a
ge
s
.
R
e
s
ult
of
thi
s
pr
oc
e
s
s
s
hown
in
F
igur
e
3
(
b)
.
T
he
ne
xt
pr
oc
e
s
s
is
h
is
togr
a
m
leve
li
ng
,
f
or
mappin
g
the
gr
a
ys
c
a
le
of
th
e
s
pinal
ve
r
tebr
a
's
x
-
r
a
y
im
a
ge
.
Af
ter
that,
c
onti
nue
the
p
r
oc
e
s
s
of
c
lahe
a
nd
media
n
f
il
ter
.
T
his
r
e
s
e
a
r
c
h
s
a
me
with
a
r
ti
c
le
[
27]
us
e
s
the
media
n
f
il
ter
ing
to
do
im
a
ge
pr
e
pr
oc
e
s
s
ing.
T
he
pur
pos
e
of
thi
s
p
r
oc
e
s
s
is
to
incr
e
a
s
e
the
c
ontr
a
s
t
in
the
im
a
ge
,
s
o
that
the
bone
s
tr
uc
tur
e
in
the
s
pinal
ve
r
tebr
a
is
s
e
e
n
mo
r
e
c
l
e
a
r
ly
.
T
he
r
e
s
ult
s
of
thi
s
pr
oc
e
s
s
a
r
e
s
hown
in
F
igur
e
3
(
c
)
.
T
he
f
inal
pr
oc
e
s
s
in
pr
e
-
pr
oc
e
s
s
ing
is
binar
yiza
ti
on
of
x
-
r
a
y
im
a
ge
s
.
T
he
pur
pos
e
of
thi
s
p
r
oc
e
s
s
is
to
identif
y
the
e
xis
t
e
nc
e
of
o
bjec
ts
us
ing
thr
e
s
holdi
ng,
whe
r
e
the
obje
c
t
will
be
logi
c
1,
a
n
d
ba
c
kgr
ound
logi
c
0.
L
ogic
1
is
white,
logi
c
0
is
blac
k.
T
he
r
e
s
ult
s
of
thi
s
pr
oc
e
s
s
a
r
e
s
hown
in
F
igur
e
3
(
d)
.
(
a
)
(
b)
F
igur
e
2.
(
a
)
Or
igi
na
l
im
a
ge
,
(
b)
C
r
opping
r
e
s
ult
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
1693
-
6930
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
,
Vol.
18
,
No
.
1
,
F
e
br
ua
r
y
2020
:
148
-
155
152
(
a
)
(
b)
(
c
)
(
d)
F
igur
e
3.
(
a
)
C
r
oppin
g
r
e
s
ult
,
(
b)
R
e
s
ize
a
nd
gr
a
y
i
mage
,
(
c
)
C
lahe
a
nd
media
n
f
il
ter
,
(
d
)
B
inar
y
im
a
g
e
Af
ter
obtaining
the
r
e
s
ult
s
,
c
onti
nue
the
ne
xt
s
tage
is
a
s
tatis
ti
c
a
l
c
a
lcula
ti
on
f
o
r
index
-
S
ingh
c
las
s
if
ica
ti
on.
R
e
s
ult
of
s
tatis
ti
c
a
l
I
nde
x
-
S
ingh
of
50
da
ta
s
tamps
o
f
t
he
la
r
ge
s
t
white
pixel
x
-
r
a
y
i
mage
s
is
7,
983
p
ixels
a
nd
the
s
malles
t
white
pixel
is
5
,
4
10
pixels
.
T
he
r
e
s
ult
s
of
e
a
c
h
va
lue
o
f
white
pi
xe
ls
a
r
e
gr
oupe
d
int
o
s
ix
pa
r
ts
of
I
nde
x
-
S
ingh
of
gr
a
de
1
to
gr
a
de
6
,
r
e
s
pe
c
ti
ve
ly.
T
he
r
e
s
ult
of
the
s
tatis
ti
c
a
l
I
nde
x
-
S
ingh
pr
oc
e
s
s
s
hown
in
T
a
ble
1
.
W
hit
e
pi
xe
ls
in
T
a
ble
1
c
a
n
be
int
e
r
pr
e
ted
to
r
e
p
r
e
s
e
nt
the
de
ns
it
y
bone
of
s
pinal
ve
r
tebr
a
.
T
he
number
of
whi
te
pi
xe
ls
to
S
ingh
I
nde
x
c
a
n
be
gr
oupe
d
int
o
gr
a
de
s
1
thr
ough
gr
a
de
6.
T
he
tot
a
l
nu
mber
of
white
pixels
a
ga
ins
t
S
i
ngh
I
nde
x
is
s
hown
in
T
a
ble
2
.
Ove
r
a
ll
in
e
a
c
h
gr
oup
of
I
nde
x
-
S
ingh
1
to
I
nde
x
-
S
in
gh
6
it
wa
s
s
e
e
n
that
in
e
a
c
h
gr
oup
the
numbe
r
of
e
a
c
h
s
ubjec
t
of
t
he
mos
t
c
omm
on
white
pixel
da
ta
in
the
gr
oup
wa
s
in
the
c
a
tegor
y
of
os
teopor
os
is
.
Ove
r
a
ll
pixel
whitene
s
s
in
e
a
c
h
gr
oup
is
in
the
c
a
tegor
y
o
f
os
teope
nia
a
nd
no
r
mal.
T
a
ble
1.
R
e
s
ult
of
s
tatis
ti
c
a
l
a
nd
I
nde
x
-
S
ingh
No
W
hi
te
P
ix
e
l
B
la
c
k P
ix
e
l
%
W
hi
te
P
ix
e
l
I
nde
x
-
S
in
gh
No
W
hi
te
P
ix
e
l
B
la
c
k P
ix
e
l
%
W
hi
te
P
ix
e
l
I
nde
x
-
S
in
gh
1
2
3
4
5
6
7
8
9
10
6
,
266
6
,
215
6
,
153
6
,
151
6
,
109
6
,
083
6
,
001
5
,
966
5
,
921
5
,
410
9
,
864
9
,
915
9
,
977
9
,
979
10
,
021
10
,
047
10
,
129
10
,
164
10
,
209
10
,
720
38.85%
38.53%
38.15%
38.13%
37.87%
37.71%
37.20%
36.99%
36.71%
33.54%
1
1
2
3
4
5
6
7
8
6
,
998
6
,
980
6
,
976
6
,
964
6
,
893
6
,
850
6
,
794
6
,
778
9
,
132
9
,
150
9
,
154
9
,
166
9
,
237
9
,
280
9
,
336
9
,
352
43.38%
43.27%
43.25%
43.17%
42.73%
42.47%
42.12%
42.02%
4
1
2
3
4
5
6
7
8
7
,
219
7
,
192
7
,
103
7
,
082
7
,
059
7
,
028
7
,
016
7
,
001
8
,
911
8
,
938
9
,
027
9
,
048
9
,
071
9
,
102
9
,
114
9
,
129
44.76%
44.59%
44.04%
43.91%
43.76%
43
.57%
43.50%
43.40%
5
1
2
3
4
5
6
7
8
6
,
512
6
,
492
6
,
477
6
,
444
6
,
421
6
,
379
6
,
356
6
,
323
9
,
618
9
,
638
9
,
653
9
,
686
9
,
709
9
,
751
9
,
774
9
,
807
40.37%
40.25%
40.15%
39.95%
39.81%
39.55%
39.40%
39.20%
2
1
2
3
4
5
6
7
8
7
,
983
7
,
862
7
,
620
7
,
571
7
,
487
7
,
426
7
,
339
7
,
338
8
,
147
8
,
268
8
,
510
8
,
559
8
,
643
8
,
704
8
,
791
8
,
792
49.49%
48.74%
47.24%
46.94%
46.42%
46.04%
45.50%
45.49%
6
1
2
3
4
5
6
7
8
6
,
747
6
,
711
6
,
708
6
,
645
6
,
630
6
,
544
6
,
543
6
,
520
9
,
383
9
,
419
9
,
422
9
,
485
9
,
500
9
,
586
9
,
587
9
,
610
41.83%
41.61%
41.59%
41.20%
41.10%
40.57%
40.56%
40.42%
3
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
V
e
r
tebr
a
os
teopor
o
s
is
de
tec
ti
on
bas
e
d
on
bone
de
ns
it
y
us
ing
I
nde
x
-
Singh
…
(
Sis
w
o
W
ar
doy
o
)
153
T
a
ble
2.
Numbe
r
o
f
white
pixels
a
ga
ins
t
I
nde
x
-
S
ingh
No
I
nde
x
-
S
in
gh 1
I
nde
x
-
S
in
gh 2
I
nde
x
-
S
in
gh 3
I
nde
x
-
S
in
gh 4
I
nde
x
-
S
in
gh 5
I
nde
x
-
S
in
gh 6
1
2
3
4
5
6
7
8
9
10
6,266
6,215
6,153
6,151
6,109
6,083
6,001
5,966
5
,921
5,410
6,512
6,492
6,477
6,444
6,421
6,379
6,356
6,323
-
-
6,747
6,711
6,708
6,645
6,630
6,544
6,543
6,520
-
-
6,998
6,980
6,976
6,964
6,893
6,850
6,794
6,778
-
-
7,219
7,192
7,103
7,082
7,059
7,028
7,016
7,001
-
-
7,983
7,862
7,620
7,571
7,487
7,426
7
,339
7,338
-
-
T
he
pe
r
c
e
ntage
va
lue
of
e
a
c
h
white
pixel
of
x
-
r
a
y
im
a
ge
of
the
s
pinal
ve
r
tebr
a
is
s
hown
in
T
a
ble
3.
T
ha
t
r
a
nge
s
f
r
om
33.
54
-
38.
85
%
indi
c
a
tes
that
the
va
lue
is
a
gr
oup
of
I
nde
x
-
S
ingh
1
in
the
c
a
tegor
y
of
os
teopor
os
is
gr
oup.
B
one
de
ns
it
y
is
r
e
duc
e
d,
whi
c
h
mak
e
s
bone
s
po
r
ous
a
nd
br
it
t
le
a
nd
they
br
e
a
k
e
a
s
il
y.
I
n
I
nde
x
-
S
ingh
2
,
I
nde
x
-
S
ingh
3
,
I
nde
x
-
S
ingh
4,
a
nd
I
nde
x
-
S
ingh
5
wi
th
pe
r
c
e
ntage
of
r
a
nge
va
l
ue
f
r
om
39.
20
-
44.
76%
be
long
to
c
a
tegor
y
of
os
teope
nia
g
r
oup.
Os
teope
nia
c
a
tegor
y
is
whe
n
you
r
bone
s
a
r
e
we
a
ke
r
than
nor
mal
but
not
s
o
f
a
r
gone
that
they
br
e
a
k
e
a
s
il
y,
whic
h
is
the
ha
ll
mar
k
of
os
teopor
os
is
.
P
e
r
c
e
ntage
va
lue
with
r
a
nge
45
.
49
-
49.
49%
a
ve
r
a
ge
va
lue
in
g
r
oup
include
d
in
I
nde
x
-
S
ingh
6
with
nor
mal
c
a
tegor
y.
T
a
ble
3.
P
e
r
c
e
ntage
va
lue
a
g
a
ins
t
I
nde
x
-
S
ingh
No
I
nde
x
-
S
in
gh
1
I
nde
x
-
S
in
gh
2
I
nde
x
-
S
in
gh
3
I
nde
x
-
S
in
gh
4
I
nde
x
-
S
in
gh
5
I
nde
x
-
S
in
gh
6
1
2
3
4
5
6
7
8
9
10
38
.
85%
38
.
53%
38
.
15%
38
.
13%
37
.
87%
37
.
71%
37
.
20%
36
.
99%
36.
71%
33
.
54%
40
.
37%
40
.
25%
40
.
15%
39
.
95%
39
.
81%
39
.
55%
39
.
40%
39
.
20%
-
-
41
.
83%
41
.
61%
41
.
59%
41
.
20%
41
.
10%
40
.
57%
40
.
56%
40
.
42%
-
-
43
.
38%
43
.
27%
43
.
25%
43
.
17%
42
.
73%
42
.
47%
42
.
12%
42
.
02%
-
-
44
.
76%
44
.
59%
44
.
04%
43
.
91%
43
.
76%
43
.
57%
43
.
50%
43
.
40%
-
-
49
.
49%
48
.
74%
47
.
24%
46
.
94%
46
.
42%
46
.
04%
45
.
50%
45
.
49%
-
-
T
e
s
ti
ng
with
50
da
ta
s
a
mpl
e
s
is
s
hown
in
T
a
ble
4
.
C
ompar
is
on
be
twe
e
n
or
thopedis
t
va
li
da
ted
da
ta
with
tes
t
s
ys
tem
da
ta
ther
e
a
r
e
s
im
il
a
r
it
ies
of
38
s
a
mpl
e
s
with
os
teopor
os
is
,
os
teope
nia,
a
n
d
nor
mal
jus
ti
f
ica
ti
on
c
a
tegor
ies
.
I
n
a
ddit
ion
,
ther
e
a
r
e
dif
f
e
r
e
nc
e
s
be
twe
e
n
or
thopedis
t
va
li
da
ti
on
da
ta
a
nd
s
y
s
tem
tes
t
da
ta
of
12
s
a
mpl
e
s
,
be
c
a
us
e
r
e
s
ult
o
f
s
a
mpl
e
f
r
om
s
ys
tem
da
ta
tes
t
is
not
s
a
me
wi
th
the
va
li
da
ti
on
r
e
s
ult
s
of
im
a
ge
da
ta
jus
ti
f
ica
ti
on
whic
h
is
pa
r
a
mete
r
o
f
s
uc
c
e
s
s
tes
t
of
a
s
ys
tem.
T
he
pe
r
c
e
ntage
a
c
c
u
r
a
c
y
of
the
s
ys
tem
tes
t
da
ta
with
the
doc
to
r
va
li
da
ti
on
da
ta
is
obtaine
d
by
the
f
oll
owing
e
qua
ti
on:
=
100%
A
=
38
50
x
100%
=
76
%
S
o,
the
e
nd
r
e
s
ult
of
thi
s
os
teopor
os
is
de
tec
ti
on
a
ids
s
ys
tem
ha
s
a
n
a
c
c
ur
a
c
y
of
76%
a
nd
a
s
ys
tem
e
r
r
or
of
24%
.
I
n
the
pr
oc
e
s
s
of
c
a
lcula
ti
ng
the
a
c
c
ur
a
c
y
that
ha
s
be
e
n
obtaine
d,
whic
h
is
take
n
f
r
om
the
r
e
s
ult
of
the
s
a
me
tes
t
da
ta
s
a
mpl
e
x
-
r
a
y
im
a
ge
divi
d
e
d
by
the
tot
a
l
number
of
x
-
r
a
y
im
a
ge
da
ta
tes
t
of
50
s
a
mpl
e
s
.
T
he
whole
s
a
mpl
e
ha
s
be
e
n
pr
oc
e
s
s
e
d
int
o
the
i
mage
pr
oc
e
s
s
ing
a
nd
us
ing
the
c
a
lcula
ti
on
of
the
a
r
e
a
on
x
-
r
a
y
im
a
ge
s
of
the
s
pinal
ve
r
tebr
a
that
r
e
s
ult
s
in
the
number
of
white
pixels
a
nd
blac
k
pixels
.
Ove
r
a
ll
the
pe
r
c
e
ntage
of
white
pixel
va
lues
a
ga
ins
t
s
ingh
index
f
r
om
gr
a
de
1
to
gr
a
de
6
c
a
n
d
e
ter
mi
ne
the
c
las
s
if
ica
ti
on
of
os
teopor
s
is
,
os
teope
ni
a
,
a
nd
nor
mal
bone
.
T
his
pr
oc
e
s
s
us
e
s
a
s
e
tup
va
lue
that
is
c
a
lcula
ted
thr
ough
a
r
a
nge
o
f
a
ve
r
a
ge
v
a
lues
,
s
o
th
a
t
a
n
a
ve
r
a
ge
va
lue
of
les
s
than
39.
20%
is
os
teopr
o
s
is
bone
an
d
if
the
a
ve
r
a
ge
va
lue
e
xc
e
e
d
s
44.
76%
then
the
bone
is
nor
mal
a
nd
the
a
ve
r
a
ge
va
lue
mi
dd
le
r
a
nge
be
twe
e
n
39.
20
-
44.
76%
then
os
teope
nia.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
1693
-
6930
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
,
Vol.
18
,
No
.
1
,
F
e
br
ua
r
y
2020
:
148
-
155
154
T
a
ble
4.
C
ompar
a
ti
ve
table
va
li
da
ti
on
a
nd
s
ys
tem
t
e
s
t
No
%
W
hi
te
pi
xe
l
J
us
ti
f
ic
a
ti
on of
da
ta
f
r
om or
th
ope
di
s
t
S
ys
te
m
te
s
t
da
ta
S
ys
te
m
te
s
t
r
e
s
ul
ts
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
38
.
85%
42
.
12%
43
.
27%
40
.
42%
37
.
71%
43
.
76%
39
.
95%
45
.
49%
41
.
20%
36
.
71%
43
.
50%
46
.
94%
47
.
24%
41
.
83%
46
.
42%
40.
25%
43
.
57%
44
.
59%
37
.
20%
43
.
38%
41
.
10%
45
.
50%
40
.
56%
39
.
40%
37.
87%
3
9
.
55%
49
.
49%
43.
91%
43
.
17%
42
.
47%
38
.
13%
41
.
61%
44
.
04%
39
.
81%
44
.
76%
48
.
74%
42
.
02%
33.
54%
36
.
99%
43
.
25%
46
.
04%
38.
53%
41
.
59%
40
.
37%
38
.
15%
40
.
15%
43
.
40%
40
.
57%
39
.
20%
42
.
73%
O
s
te
opor
os
is
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
opor
os
is
O
s
te
ope
ni
a
O
s
te
ope
ni
a
N
or
ma
l
O
s
te
ope
ni
a
O
s
te
opor
os
is
O
s
te
ope
ni
a
O
s
te
opor
os
is
O
s
te
opor
os
is
O
s
te
ope
ni
a
O
s
te
opor
os
is
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
ope
ni
a
N
or
ma
l
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
opor
os
is
N
or
ma
l
O
s
te
ope
ni
a
O
s
te
opor
os
is
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
opor
os
is
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
opor
os
is
O
s
te
ope
ni
a
O
s
te
opor
os
is
O
s
te
opor
os
is
O
s
te
ope
ni
a
O
s
te
opor
os
is
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
op
e
ni
a
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
opor
os
is
O
s
te
opor
os
is
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
opor
os
is
O
s
te
ope
ni
a
O
s
te
ope
ni
a
N
or
ma
l
O
s
te
ope
ni
a
O
s
te
opor
os
is
O
s
te
ope
ni
a
N
or
ma
l
N
or
ma
l
O
s
te
ope
ni
a
N
or
ma
l
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
op
or
os
is
O
s
te
ope
ni
a
O
s
te
ope
ni
a
N
or
ma
l
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
opor
os
is
O
s
te
ope
ni
a
N
or
ma
l
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
opor
os
is
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
ope
ni
a
N
or
ma
l
O
s
te
ope
ni
a
O
s
te
opor
os
is
O
s
te
opor
os
is
O
s
te
ope
ni
a
N
or
ma
l
O
s
te
opor
os
is
Os
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
opor
os
is
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
ope
ni
a
O
s
te
ope
ni
a
T
r
ue
T
r
ue
T
r
ue
T
r
ue
T
r
ue
T
r
ue
T
r
ue
T
r
ue
T
r
ue
T
r
ue
T
r
ue
F
a
ls
e
F
a
ls
e
T
r
ue
F
a
ls
e
T
r
ue
T
r
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T
r
ue
F
a
ls
e
T
r
ue
T
r
ue
F
a
ls
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F
a
ls
e
T
r
ue
T
r
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T
r
ue
F
a
ls
e
T
r
ue
T
r
ue
T
r
ue
T
r
ue
T
r
ue
T
r
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T
r
ue
T
r
ue
F
a
ls
e
T
r
ue
T
r
ue
T
r
ue
T
r
ue
F
a
ls
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F
a
ls
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T
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T
r
ue
F
a
ls
e
T
r
ue
T
r
ue
T
r
ue
T
r
ue
F
a
ls
e
4.
CONC
L
USI
ON
T
his
r
e
s
e
a
c
h
wa
s
s
uc
c
e
s
s
f
uly
de
ve
loped
the
ve
te
r
ba
os
teopor
os
is
de
tec
ti
on
ba
s
e
d
on
bone
de
ns
it
y
us
ing
I
nde
x
-
S
ingh
s
tatis
ti
c
a
l
blende
d
method.
T
he
s
t
a
ti
s
ti
c
a
l
I
nde
x
-
S
ingh
blende
d
method
on
x
-
r
a
y
i
mage
s
of
the
s
pinal
ve
r
tebr
a
c
a
n
be
us
e
d
f
or
s
or
t
ing
a
nd
c
las
s
if
ying
white
pixels
in
os
teopor
os
is
-
leve
l
de
tec
ti
on.
T
he
r
e
s
ult
of
the
number
of
white
pixels
in
the
c
a
l
c
ulations
ba
s
e
d
on
the
a
r
e
a
of
c
olum
ns
a
nd
r
ows
of
the
a
r
e
a
is
the
a
mount
or
de
ns
it
y
of
the
bone
mas
s
of
the
s
pinal
ve
r
tebr
a
.
I
n
othe
r
wor
ds
,
bone
de
ns
it
y
is
r
e
pr
e
s
e
nted
by
the
number
of
white
pixels
of
a
n
a
r
e
a
.
Ove
r
a
l
l,
the
r
e
s
ult
s
of
tes
ti
ng
the
os
teopor
os
is
de
tec
ti
on
s
ys
tem
ha
ve
be
e
n
s
uc
c
e
s
s
f
ul
a
nd
c
a
n
be
us
e
d
a
s
a
n
e
a
r
ly
de
tec
ti
on
s
ys
tem
f
or
os
teopor
os
is
.
T
his
a
s
s
is
tanc
e
s
ys
tem
ha
s
a
de
tec
ti
on
a
c
c
ur
a
c
y
of
76%
c
ompar
e
d
to
do
c
tor
's
jus
ti
f
ica
ti
on.
T
he
a
c
c
ur
a
c
y
o
f
os
teopor
os
is
de
tec
ti
on
s
ys
tem
thi
s
r
e
s
e
a
r
c
h
ne
e
ds
to
be
im
pr
ove
d
by
incr
e
a
s
ing
the
s
a
mpl
e
a
nd
the
qua
li
ty
of
p
r
e
-
pr
oc
e
s
s
ing.
AC
KNOWL
E
DGE
M
E
NT
S
T
ha
nk
you
to
the
I
ns
ti
tut
e
of
M
a
na
ge
ment
E
duc
a
ti
on
F
und
(
L
P
DP)
I
ndone
s
ia:
w
ho
ha
s
f
inanc
e
d
th
is
r
e
s
e
a
r
c
h
unti
l
publi
s
he
d
int
o
a
n
a
r
ti
c
le
in
T
E
L
KO
M
NI
KA
T
e
lec
omm
unica
ti
on
C
omput
ing
,
E
lec
tr
onics
a
nd
C
ontr
ol
J
our
na
l.
Evaluation Warning : The document was created with Spire.PDF for Python.
T
E
L
KO
M
NI
KA
T
e
lec
omm
un
C
omput
E
l
C
ontr
o
l
V
e
r
tebr
a
os
teopor
o
s
is
de
tec
ti
on
bas
e
d
on
bone
de
ns
it
y
us
ing
I
nde
x
-
Singh
…
(
Sis
w
o
W
ar
doy
o
)
155
RE
F
E
RE
NC
E
S
[1
]
T
.
D
.
Rach
n
er,
S.
K
h
o
s
l
a,
an
d
L
.
C.
H
o
fb
au
er,
“O
s
t
eo
p
o
ro
s
i
s
:
N
o
w
an
d
T
h
e
Fu
t
u
re,
”
Th
e
La
n
ce
t
,
v
o
l
.
3
7
7
,
n
o
.
9
7
7
3
,
p
p
.
1
2
7
6
–
1
2
8
7
,
2
0
1
1
.
[2
]
J
.
T
.
Pramu
d
i
t
o
,
S.
So
e
g
i
j
o
k
o
,
T
.
R.
Men
g
k
o
,
F.
I.
Mu
c
h
t
a
d
i
,
an
d
R.
G
.
W
ach
j
u
d
i
,
“T
ra
b
ecu
l
ar
Pa
t
t
er
n
A
n
al
y
s
i
s
o
f
Pro
x
i
m
al
Femu
r
Ra
d
i
o
g
ra
p
h
s
fo
r
O
s
t
e
o
p
o
ro
s
i
s
D
et
ec
t
i
o
n
,
”
J.
B
i
o
m
ed
.
P
h
a
r
m
.
E
n
g
.
,
v
o
l
.
1
,
n
o
.
1
,
p
p
.
4
5
–
5
1
,
2
0
0
7
.
[3
]
D
.
R.
C
.
Sarah
H
.
G
u
el
g
n
er,
T
.
N
.
G
rab
o
,
an
d
E
.
D
.
N
ew
man
,
"
O
s
t
e
o
p
o
ro
s
i
s
:
Cl
i
n
i
cal
G
u
i
d
e
l
i
n
es
fo
r
Prev
e
n
t
i
o
n
,
D
i
a
g
n
o
s
i
s
,
an
d
Man
ag
eme
n
t
,
"
Sp
i
n
g
e
r
Pu
b
l
i
s
h
i
n
g
Co
mp
an
y
,
2
0
0
7
.
[4
]
S.
L
es
t
ari
,
G
.
B.
Su
p
ar
t
a,
an
d
N
.
K
ert
i
a,
“T
h
e
Co
rr
el
at
i
o
n
b
et
w
een
T
ex
t
u
r
e
Paramet
er
o
f
Ma
n
d
i
b
l
e
T
ra
b
ec
u
l
l
ar
Bo
n
e
w
i
t
h
t
h
e
Bo
n
e
Mas
s
D
en
s
i
t
y
V
a
l
u
e,
”
In
t
.
S
y
m
p
h
o
s
i
u
m
A
d
v.
Cl
i
n
.
A
p
p
r
o
a
ch
P
r
ev.
D
en
t
.
Ca
r
i
e
s
Im
p
l
i
c
a
t
ed
Dis,
p
p
.
1
–
4
,
2
0
1
2
.
[5
]
E
.
I.
Sel
a,
et
a
l
.
,
“Feat
u
re
Sel
ec
t
i
o
n
o
f
t
h
e
C
o
mb
i
n
a
t
i
o
n
o
f
P
o
ro
u
s
T
rab
e
cu
l
ar
w
i
t
h
A
n
t
h
ro
p
o
me
t
ri
c
Feat
u
res
fo
r
O
s
t
eo
p
o
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o
s
i
s
Screen
i
n
g
,
”
In
t
.
J.
E
l
ec
t
r
.
Co
m
p
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t
.
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n
g
.
,
v
o
l
.
5
,
n
o
.
1
,
p
p
.
7
8
–
8
3
,
2
0
1
5
.
[6
]
N
.
Sh
an
k
ar,
et
a
l
.
,
“Co
m
p
ari
s
o
n
o
f
S
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s
In
d
ex
w
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y
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b
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met
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y
(
D
X
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)
i
n
E
v
al
u
at
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n
g
Po
s
t
-
Me
n
o
p
a
u
s
a
l
O
s
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o
p
o
ro
s
i
s
,
”
3
rd
I
n
t
e
r
n
a
t
i
o
n
a
l
Co
n
f
e
r
en
ce
o
n
E
l
ec
t
r
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n
i
cs
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o
m
p
u
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Te
ch
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(ICE
CT
2
0
1
1
)
,
p
p
.
3
6
1
–
3
6
4
,
2
0
1
1
.
[7
]
K
.
I.
A
l
ex
an
d
ra
k
i
,
et
al
.
,
“T
h
e
K
n
o
w
l
ed
g
e
o
f
O
s
t
e
o
p
o
ro
s
i
s
Ri
s
k
Fact
o
rs
i
n
a
G
reek
Femal
e
Po
p
u
l
at
i
o
n
,
”
M
a
t
u
r
i
t
a
s
,
v
o
l
.
5
9
,
n
o
.
1
,
p
p
.
3
8
–
4
5
,
2
0
0
8
.
[8
]
V
.
H
eg
d
e,
J
.
E
.
J
o
,
P.
A
n
d
re
o
p
o
u
l
o
u
,
an
d
J
.
M.
L
an
e,
“E
ffect
o
f
O
s
t
e
o
p
o
ro
s
i
s
Me
d
i
ca
t
i
o
n
s
o
n
Frac
t
u
re
H
eal
i
n
g
,
”
O
s
t
eo
p
o
r
o
s
i
s
I
n
t
e
r
n
a
t
i
o
n
a
l
,
v
o
l
.
2
7
,
n
o
.
3,
p
p
.
8
6
1
-
8
7
1
,
2
0
1
6
.
[9
]
In
d
o
n
e
s
i
a
n
Mi
n
i
s
t
ry
o
f
H
ea
l
t
h
,
“
O
s
t
e
o
p
o
ro
s
i
s
i
n
In
d
o
n
es
i
a
(i
n
Bah
a
s
a:
K
o
n
d
i
s
i
Pen
y
ak
i
t
O
s
t
e
o
p
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ro
s
i
s
d
i
In
d
o
n
e
s
i
a),
”
In
f
o
d
a
t
i
n
-
In
d
o
n
es
i
an
Mi
n
i
s
t
r
y
o
f
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e
al
t
h
D
a
t
a
an
d
I
n
fo
rma
t
i
o
n
Cen
t
er
,
2
0
1
5
.
[1
0
]
Y
.
Pu
rw
o
s
u
n
u
,
et
,
al
.
“V
i
t
am
i
n
K
2
T
rea
t
men
t
fo
r
Po
s
t
m
en
o
p
au
s
al
O
s
t
e
o
p
o
ro
s
i
s
i
n
In
d
o
n
e
s
i
a,
”
J.
O
b
s
t
et
.
G
y
n
a
ec
ol.
R
es
.
,
v
o
l
.
3
2
,
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o
.
2
,
p
p
.
2
3
0
–
2
3
4
,
2
0
0
6
.
[1
1
]
J
.
L
ó
p
ez
-
L
ó
p
ez,
et
al
,
“E
arl
y
D
i
a
g
n
o
s
i
s
o
f
O
s
t
eo
p
o
r
o
s
i
s
b
y
mean
s
o
f
O
r
t
h
o
p
a
n
t
o
mo
g
rams
a
n
d
O
ra
l
X
-
R
ay
s
:
A
Sy
s
t
emat
i
c
Rev
i
ew
,
”
M
e
d
.
O
r
a
l
P
a
t
o
l
O
r
a
l
Ci
r
.
B
u
c
a
l
,
v
o
l
.
1
6
,
n
o
.
7
,
J
u
l
y
2
0
1
1
.
[1
2
]
M.
L
o
ren
t
zo
n
an
d
S.
R.
Cu
mmi
n
g
s
,
“O
s
t
eo
p
o
r
o
s
i
s
:
T
h
e
ev
o
l
u
t
i
o
n
o
f
a
d
i
a
g
n
o
s
i
s
,
”
J.
In
t
er
n
.
M
ed
.
,
v
o
l
.
2
7
7
,
n
o
.
6
,
p
p
.
6
5
0
–
6
6
1
,
J
u
n
e
2
0
1
5
.
[1
3
]
S.
I.
Raj
a
Ir
fan
Q
o
d
i
r,
“Si
n
g
h
In
d
ex
A
ccu
rac
y
,
”
J.
M
ed
.
S
ci
.
,
v
o
l
.
2
4
,
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o
.
1
,
p
p
.
1
2
–
1
5
,
2
0
1
6
.
[1
4
]
S.
R.
Cu
mmi
n
g
s
et
al
.
,
“
Bo
n
e
D
en
s
i
t
y
at
V
ar
i
o
u
s
Si
t
e
s
fo
r
Pred
i
ct
i
o
n
o
f
H
i
p
Fract
u
res
,
”
T
h
e
L
an
cet
,
v
o
l
.
3
4
1
,
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o
.
8
8
3
7
,
p
p
.
7
2
–
7
5
,
1
9
9
3
.
[1
5
]
D
.
Marcel
o
an
d
W
.
Si
l
v
a,
“Co
mmen
t
ary
D
i
ag
n
o
s
i
s
o
f
O
s
t
e
o
p
o
ro
s
i
s
:
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n
e
Mi
n
era
l
D
en
s
i
t
y
,
Ri
s
k
Fact
o
r
s
,
o
r
Bo
t
h
?,
”
E
C
O
r
t
h
o
p
a
ed
i
cs
,
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o
l
.
7
,
p
p
.
5
0
0
–
5
0
2
,
2
0
1
8
.
[1
6
]
M.
Z
ey
t
i
n
o
g
l
u
,
R.
K
.
J
ai
n
,
an
d
T
.
J
.
V
o
k
es
,
“V
er
t
e
b
ral
Fract
u
re
A
s
s
e
s
s
me
n
t
:
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n
h
a
n
ci
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g
t
h
e
D
i
ag
n
o
s
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s
,
Prev
e
n
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i
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n
,
an
d
T
rea
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men
t
o
f
O
s
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e
o
p
o
ro
s
i
s
,
”
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o
n
e
,
v
o
l
.
1
0
4
,
p
p
.
5
4
-
65
,
N
o
v
2
0
1
7
.
[1
7
]
W
H
O
,
“A
s
s
es
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[1
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]
J
.
N
a’am,
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.
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arl
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en
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a,
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.
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mag
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mp
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3
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p
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.
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.
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.
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.
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.
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R.
W
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.
[2
6
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T
.
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.
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,
an
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L
ei
,
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LKO
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NIKA
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4
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.
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4
.
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