I
nd
o
ne
s
ia
n J
o
urna
l o
f
E
lect
rica
l En
g
ineering
a
nd
Co
m
p
u
t
er
Science
Vo
l.
10
,
No
.
1
,
A
p
r
il
2
0
1
8
,
p
p
.
201
~
206
I
SS
N:
2502
-
4752
,
DOI
: 1
0
.
1
1
5
9
1
/
i
j
ee
cs
.
v
10
.i
1
.
p
p
201
-
2
0
6
201
J
o
ur
na
l ho
m
ep
a
g
e
:
h
ttp
:
//ia
e
s
co
r
e.
co
m/jo
u
r
n
a
ls
/in
d
ex
.
p
h
p
/
ijeec
s
Wa
v
elet
-
Ba
sed W
eig
hted
M
edia
n
F
ilter
for I
m
a
g
e
De
no
ising
of
M
RI Bra
in I
m
a
g
e
s
N.
Ra
j
a
la
k
s
h
m
i
1
,
K
.
Na
ra
y
a
na
n
2
,
P
.
A
m
ud
ha
v
a
lli
3
1
De
p
a
rtme
n
t
o
f
Bio
m
e
d
ica
l
En
g
g
,
Ka
rp
a
g
a
m
A
c
a
d
e
m
y
o
f
Hi
g
h
e
r
Ed
u
c
a
ti
o
n
,
C
o
im
b
a
to
re
-
In
d
ia
2
In
stit
u
te
o
f
Ro
a
d
a
n
d
T
ra
n
sp
o
rt
T
e
c
h
n
o
lo
g
y
,
Ero
d
e
-
In
d
ia
3
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter S
c
ien
c
e
a
n
d
E
n
g
in
e
e
rin
g
,
Ka
rp
a
g
a
m
Ac
a
d
e
m
y
o
f
Hig
h
e
r
Ed
u
c
a
ti
o
n
,
C
o
i
m
b
a
to
re
-
In
d
ia
Art
icle
I
nfo
AB
ST
RAC
T
A
r
ticle
his
to
r
y:
R
ec
eiv
ed
J
an
9
,
2
0
1
8
R
ev
i
s
ed
Mar
2
,
2
0
1
8
A
cc
ep
ted
Mar
18
,
2
0
1
8
P
re
li
m
in
a
ry
d
iag
n
o
sin
g
o
f
M
RI
ima
g
e
s
f
ro
m
th
e
h
o
sp
it
a
l
c
a
n
n
o
t
b
e
re
li
e
d
o
n
b
e
c
a
u
se
o
f
th
e
c
h
a
n
c
e
s
o
f
o
c
c
u
rre
n
c
e
o
f
a
rti
f
a
c
ts
r
e
su
lt
in
g
in
d
e
g
ra
d
e
d
q
u
a
li
ty
o
f
i
m
a
g
e
,
w
h
il
e
o
th
e
rs
m
a
y
b
e
c
o
n
f
u
se
d
w
it
h
p
a
th
o
lo
g
y
.
Ob
tain
e
d
M
RI
im
a
g
e
u
su
a
ll
y
c
o
n
tain
s
l
imited
a
rti
f
a
c
ts.
It
b
e
c
o
m
e
s
c
o
m
p
l
e
x
o
n
e
f
o
r
d
o
c
to
rs
i
n
a
n
a
ly
z
in
g
th
e
m
.
B
y
in
c
re
a
sin
g
th
e
c
o
n
tras
t
o
f
a
n
i
m
a
g
e
,
it
w
il
l
b
e
e
a
s
y
to
a
n
a
l
y
z
e
.
In
o
rd
e
r
to
f
in
d
th
e
tu
m
o
r
p
a
rt
e
ff
icie
n
tl
y
M
RI
b
ra
in
ima
g
e
sh
o
u
l
d
b
e
e
n
h
a
n
c
e
d
p
r
o
p
e
rly
.
T
h
e
i
m
a
g
e
e
n
h
a
n
c
e
m
e
n
t
m
e
th
o
d
s
m
a
in
l
y
im
p
ro
v
e
th
e
v
isu
a
l
a
p
p
e
a
ra
n
c
e
o
f
M
RI
im
a
g
e
s.
T
h
e
g
o
a
l
o
f
d
e
n
o
isin
g
is
t
o
re
m
o
v
e
th
e
n
o
ise
,
w
h
ich
m
a
y
c
o
rru
p
t
a
n
im
a
g
e
d
u
rin
g
it
s
a
c
q
u
isit
io
n
o
r
tran
sm
issio
n
,
w
h
il
e
re
tain
in
g
it
s
q
u
a
li
ty
.
In
th
is
p
a
p
e
r
e
ff
e
c
ti
v
e
n
e
s
s
o
f
se
v
e
n
d
e
n
o
isi
n
g
a
lg
o
rit
h
m
s
v
iz.
m
e
d
ian
f
il
ter,
w
i
e
n
e
r
f
il
ter,
w
a
v
e
let
f
il
te
r,
w
a
v
e
let
b
a
se
d
w
ien
e
r,
N
L
M
,
w
a
v
e
let
b
a
s
e
d
NL
M
,
p
ro
p
o
se
d
w
a
v
e
let
b
a
se
d
w
e
i
g
h
ted
m
e
d
ian
f
il
ter(W
M
F
)
u
sin
g
M
RI
im
a
g
e
s
in
th
e
p
re
se
n
c
e
o
f
a
d
d
i
ti
v
e
w
h
it
e
G
a
u
ss
ian
n
o
ise
is
c
o
m
p
a
re
d
.
T
h
e
e
x
p
e
rime
n
tal
re
su
lt
s
a
re
a
n
a
ly
z
e
d
in
term
s
o
f
v
a
rio
u
s im
a
g
e
q
u
a
li
ty
m
e
tri
c
s.
K
ey
w
o
r
d
s
:
Den
o
is
i
n
g
I
m
ag
e
q
u
a
lit
y
m
etr
ic
s
Me
d
ian
f
il
ter
P
r
ep
r
o
ce
s
s
in
g
W
av
elet
Co
p
y
rig
h
t
©
2
0
1
8
In
stit
u
te o
f
A
d
v
a
n
c
e
d
E
n
g
i
n
e
e
rin
g
a
n
d
S
c
ien
c
e
.
Al
l
rig
h
ts
re
se
rv
e
d
.
C
o
r
r
e
s
p
o
nd
ing
A
uth
o
r
:
N.
R
aj
alak
s
h
m
i,
Ass
o
ciate
P
r
o
f
ess
o
r
Facu
lt
y
o
f
E
n
g
i
n
ee
r
in
g
,
Dep
ar
t
m
en
t o
f
B
io
m
ed
ical
E
n
g
g
,
Kar
p
ag
a
m
A
ca
d
e
m
y
o
f
Hi
g
h
er
E
d
u
ca
tio
n
,
C
o
i
m
b
ato
r
e
-
I
n
d
ia
.
E
m
ail:
p
r
an
ir
aj
i1
@
g
m
ai
l.c
o
m
1.
I
NT
RO
D
UCT
I
O
N
T
h
e
i
m
ag
e
ac
q
u
ir
ed
b
y
t
h
e
ac
q
u
is
itio
n
d
ev
ice
i
s
s
u
s
ce
p
tib
le
b
y
th
e
e
n
v
ir
o
n
m
en
t.
T
h
e
r
esto
r
atio
n
o
f
i
m
a
g
es
tr
ie
s
to
m
in
i
m
ize
th
e
ef
f
ec
ts
o
f
t
h
ese
d
eg
r
ad
atio
n
s
b
y
m
ea
n
s
o
f
a
f
ilter
[
1
]
.
T
h
er
ef
o
r
e,
a
f
u
n
d
a
m
en
tal
p
r
o
b
lem
i
n
t
h
e
i
m
a
g
e
p
r
o
ce
s
s
i
n
g
is
th
e
i
m
p
r
o
v
e
m
e
n
t
o
f
t
h
eir
q
u
alit
y
t
h
r
o
u
g
h
th
e
r
ed
u
ctio
n
o
f
t
h
e
n
o
i
s
e
[
2
]
.
A
g
r
ea
t
v
ar
iet
y
o
f
tec
h
n
iq
u
es
d
e
d
icate
d
to
ca
r
r
y
o
u
t
t
h
is
tas
k
ex
is
t.
E
ac
h
o
f
t
h
e
m
d
ep
en
d
s
o
n
th
e
t
y
p
es
o
f
t
h
e
n
o
is
e
i
n
i
m
ag
e
s
.
No
is
e
n
o
t
o
n
l
y
lo
w
er
s
i
m
a
g
e
q
u
al
it
y
b
u
t
also
ca
n
ca
u
s
e
f
ea
t
u
r
e
ex
tr
ac
tio
n
,
an
al
y
s
i
s
an
d
r
ec
o
g
n
itio
n
al
g
o
r
ith
m
s
to
b
e
u
n
r
eliab
le
T
h
e
M
R
I
i
m
ag
e
s
ar
e
n
o
r
m
all
y
a
f
f
ec
ted
b
y
a
t
y
p
e
o
f
n
o
is
e
ca
l
led
g
au
s
s
ia
n
No
is
e.
T
h
e
p
r
esen
c
e
o
f
n
o
is
e
h
a
m
p
er
s
d
iag
n
o
s
i
s
.
T
h
e
d
iag
n
o
s
tic
an
d
v
is
u
al
q
u
alit
y
o
f
t
h
e
MR
i
m
a
g
es
ar
e
a
f
f
ec
ted
b
y
t
h
e
n
o
is
e
ad
d
ed
w
h
ile
ac
q
u
i
s
itio
n
.
No
is
e
r
e
m
o
v
al
i
s
es
s
en
t
ial
i
n
m
ed
ical
i
m
ag
in
g
ap
p
licatio
n
s
in
o
r
d
er
to
en
h
an
ce
an
d
r
ec
o
v
er
an
ato
m
ical
d
etails
th
at
m
a
y
b
e
h
id
d
en
i
n
th
e
d
ata.
I
n
r
ec
en
t
y
ea
r
s
,
w
av
ele
t
tr
an
s
f
o
r
m
[
3
]
s
h
o
w
s
a
clea
r
ad
v
an
ta
g
e
in
t
h
e
f
ield
o
f
i
m
ag
e
d
en
o
i
s
in
g
d
o
m
ain
s
,
an
d
h
as
m
an
y
r
esear
ch
r
esu
lt
s
.
T
h
e
im
p
o
r
tan
t
p
r
o
p
er
ty
o
f
a
g
o
o
d
im
a
g
e
-
d
en
o
is
i
n
g
m
o
d
el
is
th
at
it
s
h
o
u
ld
co
m
p
let
el
y
r
e
m
o
v
e
n
o
i
s
e
as
f
ar
as
p
o
s
s
i
b
le
as
w
ell
a
s
p
r
eser
v
e
ed
g
e
s
.
T
h
e
p
ap
er
is
o
r
g
an
ized
as
f
o
llo
w
s
:
Sect
io
n
I
I
d
escr
ib
es
m
et
h
o
d
o
lo
g
y
o
f
t
h
e
p
r
o
p
o
s
ed
s
y
s
te
m
.
Sect
io
n
I
I
I
d
escr
ib
es
d
en
o
is
in
g
p
er
f
o
r
m
an
ce
m
ea
s
u
r
es
a
n
d
also
E
x
p
er
i
m
e
n
tal
r
es
u
lts
ar
e
p
r
o
v
id
ed
,
f
o
ll
o
w
ed
b
y
s
u
m
m
ar
y
,
co
n
c
lu
s
io
n
i
n
s
ec
tio
n
I
V
an
d
V.
T
h
is
p
ap
er
co
m
p
ar
es r
ec
e
n
t e
x
i
s
ti
n
g
d
e
n
o
is
in
g
s
c
h
e
m
es
w
i
th
p
r
o
p
o
s
ed
w
a
v
elet
b
ased
w
ei
g
h
ted
m
ed
ia
n
f
i
lter
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l
.
10
,
No
.
1
,
A
p
r
il
2
0
1
8
:
2
0
1
–
2
0
6
202
2.
P
RO
P
O
SE
D
WAV
E
L
E
T
B
ASE
D
W
E
I
G
H
T
E
D
M
E
DIAN F
I
L
T
E
R
T
h
is
p
r
o
p
o
s
ed
ap
p
r
o
ac
h
;
First
MR
b
r
ain
i
m
a
g
e
is
s
u
b
j
ec
ted
to
A
W
GN
n
o
is
e
a
n
d
is
d
ec
o
m
p
o
s
ed
b
y
Haa
r
w
a
v
elet
tr
an
s
f
o
r
m
p
r
o
d
u
ce
s
l
s
ca
le
s
,
an
d
th
e
n
3
l
+1
s
u
b
i
m
a
g
e
s
.
Hig
h
f
r
eq
u
e
n
c
y
s
u
b
-
i
m
a
g
e
co
n
ta
in
s
ed
g
e
f
ea
tu
r
e
o
f
an
i
m
ag
e,
d
etail
in
f
o
r
m
a
tio
n
an
d
also
n
o
is
es
m
ai
n
l
y
co
n
ce
n
tr
ated
o
n
h
ig
h
f
r
eq
u
e
n
c
y
co
m
p
o
n
e
n
t
s
.
Seco
n
d
to
p
r
eser
v
e
th
e
ed
g
es
“
s
o
b
el
m
as
k
s
[
9
]
”
ar
e
ap
p
lied
to
h
o
r
izo
n
tal,
v
er
tical
an
d
d
iag
o
n
al
s
u
b
i
m
a
g
es
;
th
e
n
ea
ch
s
u
b
i
m
ag
es
p
r
o
d
u
ce
s
b
in
ar
y
ed
g
e
p
atter
n
s
.
B
ased
o
n
th
e
b
in
ar
y
ed
g
e
m
ap
,
f
ilter
i
n
g
is
p
er
f
o
r
m
ed
.
T
h
at
i
s
,
i
f
t
h
e
p
o
s
iti
o
n
(
m
,
n
)
in
t
h
e
s
u
b
-
i
m
a
g
e
b
elo
n
g
s
to
a
n
ed
g
e,
d
en
o
is
in
g
p
r
o
ce
s
s
i
s
n
o
t
p
er
f
o
r
m
ed
.
O
n
t
h
e
o
th
er
h
an
d
,
if
t
h
e
p
o
s
itio
n
(
m
,
n
)
d
o
es
n
o
t
b
elo
n
g
to
a
n
ed
g
e,
d
en
o
is
in
g
p
r
o
ce
s
s
h
a
s
b
ee
n
p
er
f
o
r
m
ed
u
s
i
n
g
w
ei
g
h
ted
m
ed
ian
f
i
lter
w
it
h
5
x
5
m
a
s
k
o
n
ea
ch
s
u
b
i
m
a
g
e.
T
h
ir
d
co
m
b
in
e
3
b
in
ar
y
ed
g
e
m
ap
s
w
h
ic
h
h
a
v
e
p
r
o
d
u
ce
d
4
th
b
in
ar
y
ed
g
e
m
ap
u
s
i
n
g
th
i
s
,
f
ilter
i
n
g
h
as
p
er
f
o
r
m
ed
o
n
l
o
w
f
r
eq
u
en
c
y
s
u
b
i
m
a
g
e
b
y
w
e
ig
h
ted
m
ed
ia
n
f
i
lter
.
Fin
all
y
t
h
r
o
u
g
h
in
v
er
s
e
Haa
r
tr
an
s
f
o
r
m
,
t
h
e
en
h
a
n
ce
d
i
m
ag
e
i
s
o
b
tain
ed
.
T
h
e
alleg
ed
m
et
h
o
d
r
e
m
o
v
e
s
n
o
i
s
es
an
d
p
r
eser
v
es
ed
g
e
s
e
f
f
ec
t
iv
el
y
w
it
h
o
u
t
b
lu
r
r
in
g
t
h
e
d
etail
s
.
T
h
e
ex
p
er
i
m
e
n
tal
r
es
u
lt
s
d
is
clo
s
e
th
at
t
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
is
e
f
f
ec
tiv
e
in
f
il
ter
in
g
t
h
e
n
o
i
s
es
.
T
a
b
le1
s
h
o
w
s
t
h
e
p
er
f
o
r
m
a
n
ce
o
f
li
s
ted
f
il
ter
s
[
4
]
-
[
7
]
,
[
1
0
]
,
[
1
1
]
.
Fo
r
C
o
n
clu
d
in
g
,
t
h
e
b
est
f
i
lter
[
1
2
]
o
f
w
a
v
e
let
b
ased
w
ei
g
h
ted
m
ed
ian
f
ilter
is
id
en
ti
f
ied
an
d
u
s
ed
f
o
r
MR
b
r
ain
i
m
ag
e
e
n
h
an
ce
m
e
n
t.
I
t
is
u
s
ed
f
o
r
d
i
m
in
is
h
i
n
g
n
o
is
e
f
r
o
m
MRI
b
r
ain
i
m
a
g
e
a
n
d
al
s
o
p
r
eser
v
es
ed
g
es
ev
e
n
at
h
ig
h
n
o
is
e
lev
el
w
it
h
h
i
g
h
co
n
tr
ast
[
1
3
]
.
T
h
e
p
er
f
o
r
m
a
n
ce
an
al
y
s
is
o
f
t
h
e
f
ilter
s
is
co
m
p
ar
ed
in
ter
m
s
o
f
p
ea
k
-
s
ig
n
a
l
-
to
-
n
o
is
e
r
atio
,
an
d
s
i
g
n
a
l
-
to
-
n
o
i
s
e
r
atio
,
MS
E
q
u
an
tita
tiv
e
l
y
;
t
h
e
p
r
o
p
o
s
ed
m
et
h
o
d
h
as
p
r
o
d
u
ce
d
h
ig
h
P
SNR
an
d
lo
w
M
SE
co
m
p
ar
ab
le
to
o
th
er
m
et
h
o
d
s
.
Fig
u
r
e
1
,
s
h
o
w
s
d
i
f
f
er
e
n
t
d
en
o
is
in
g
m
ec
h
a
n
i
s
m
o
f
M
R
b
r
a
in
i
m
a
g
e
Fi
g
u
r
e
2.
Sh
o
w
s
d
en
o
is
i
n
g
o
u
tp
u
ts
o
f
MRI
b
r
ain
i
m
ag
e
s
co
r
r
u
p
ted
b
y
A
W
G
N
n
o
i
s
e
o
f
1
0
%
a
n
d
2
0
%
p
r
o
b
a
b
ilit
y
d
en
s
i
ties
.
F
ig
u
r
e
3
illu
s
tr
ates
t
h
e
g
r
ap
h
ical
r
ep
r
esen
tat
io
n
o
f
M
SE,
P
SNR
,
SNR
o
f
1
0
% a
n
d
2
0
% n
o
is
e
d
en
s
it
y
.
Fig
u
r
e
1
.
B
lo
ck
d
iag
r
a
m
o
f
d
if
f
er
en
t d
en
o
i
s
i
n
g
m
ec
h
an
i
s
m
o
f
MR b
r
ain
i
m
a
g
e
A
lg
o
r
ith
m
f
o
r
p
r
o
p
o
s
ed
w
eig
hted
m
e
d
ia
n w
a
ve
let
f
ilter
I
n
p
u
t: No
is
y
i
m
ag
e
N
o
f
s
ize(
m
*
n
)
Ou
tp
u
t: DN
-
>
De
n
o
is
ed
i
m
a
g
e
[
H
V
D
A
]
=
w
av
el
t_
d
ec
o
m
p
o
s
e(
N)
u
s
i
n
g
Har
r
,
W
av
elet
co
m
p
o
n
en
ts
f
o
r
ea
ch
H
V
D
A
as I
Fin
d
s
lid
i
n
g
w
in
d
o
f
o
r
I
h
ic
h
w
i
n
d
o
o
f
s
ize
3
*
3
-
> K
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
W
a
ve
let
-
B
a
s
ed
W
eig
h
ted
Med
ia
n
F
ilter
fo
r
I
ma
g
e
Den
o
is
in
g
o
f MR
I
B
r
a
in
I
m
a
g
es (
N
.
R
a
ja
la
ksh
mi
)
203
f
o
r
ea
ch
w
in
d
o
w
K
C
=
ce
n
ter
p
ix
el
o
f
K
if
C
=
m
ax
(
K)
a
n
d
c
=
m
in
(
K)
n
o
is
e
=
1
;
else
n
o
is
e
=
0
;
en
d
if
en
d
f
o
r
Fin
d
No
is
e
De
n
s
i
t
y
,
No
is
e
De
n
s
it
y
=
(
n
u
m
b
er
o
f
n
o
is
e
p
ix
e
l
/ n
u
m
b
er
o
f
p
ix
el
i
n
I
)
No
is
e
Den
s
it
y
Su
g
g
e
s
ted
W
D1
*
W
D1
0
%
p
≤
1
5
%
1
5
%
p
≤
3
0
%
3
0
%
p
≤
4
5
%
4
5
%
p
≤
6
0
%
6
0
%
p
≤
7
0
%
3
*
3
5
*
5
7
*
7
9
*
9
1
1
*
1
1
b
=e
d
g
e(
I
)
b
y
s
o
b
el
f
o
r
x
=1
to
R
o
w
s
ize
o
f
I
f
o
r
y
=
1
to
co
lu
m
n
s
ize
o
f
I
if
b
(
x
,
y
)
==
1
d
(
x
,
y
)
=
I
(
x
,
y
)
else
Fin
d
s
lid
i
n
g
w
h
ic
h
w
i
n
d
o
w
o
f
s
ize
W
D1
*
W
D1
*
-
> K
f
o
r
ea
ch
w
in
d
o
w
K
C
=
ce
n
ter
p
ix
el
o
f
K
if
C
=
m
ax
(
K)
a
n
d
C
=
m
i
n
(
K
)
d
(
x
,
y
)
=
w
ei
g
h
ter
m
ed
ia
n
f
i
lte
r
is
p
er
f
o
r
m
ed
o
n
c(
x
,
y
)
else
d
(
x
,
y
)
=
I
(
x
,
y
)
en
d
if
en
d
f
o
r
en
d
if
en
d
f
o
r
en
d
f
o
r
u
p
d
ate
H
V
D
A
b
y
d
en
d
f
o
r
Dn
=
w
av
e
let_
co
m
p
o
s
e(
H,
V,
D,
A
)
;
3.
DE
NO
I
SI
N
G
P
E
RF
O
RM
ANCE
M
E
ASURE
S
A.
P
SNR
T
h
e
P
SNR
co
m
p
u
te
s
th
e
p
ea
k
s
ig
n
al
-
to
-
n
o
is
e
r
atio
,
b
et
w
ee
n
t
w
o
i
m
a
g
es
i
n
th
e
u
n
it
o
f
d
ec
ib
els
[
1
4
]
.
T
h
is
r
atio
is
o
f
te
n
u
s
ed
as
a
q
u
alit
y
m
ea
s
u
r
e
m
e
n
t
b
et
w
ee
n
t
h
e
o
r
ig
i
n
al
a
n
d
a
co
m
p
r
es
s
ed
i
m
a
g
e.
T
h
e
h
i
g
h
er
th
e
P
SNR
,
t
h
e
b
etter
th
e
q
u
alit
y
o
f
t
h
e
co
m
p
r
ess
ed
[
8
]
o
r
r
ec
o
n
s
tr
u
cted
i
m
a
g
e.
(
)
(
1
)
B
.
M
SE
T
h
e
mea
n
s
q
u
a
r
e
err
o
r
(
MSE
)
q
u
an
ti
f
ies
t
h
e
s
tr
en
g
t
h
o
f
er
r
o
r
s
ig
n
al
a
n
d
is
ca
lcu
lated
ac
c
o
r
d
in
g
to
th
e
f
o
r
m
u
la
∑
∑
(
)
(
)
(
2
)
W
h
er
e
is
th
e
i
m
ag
e
d
i
m
e
n
s
io
n
,
(
)
an
d
(
)
r
ep
r
esen
ts
t
h
e
in
te
n
s
iti
es o
f
p
ix
el
s
(
i,
j
)
in
th
e
o
r
ig
in
a
l
i
m
a
g
e
an
d
d
en
o
is
ed
i
m
ag
e,
r
e
s
p
ec
tiv
el
y
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l
.
10
,
No
.
1
,
A
p
r
il
2
0
1
8
:
2
0
1
–
2
0
6
204
C.
SNR
T
h
e
s
ig
n
a
l
to
n
o
is
e
r
a
tio
(
S
N
R
)
d
eter
m
i
n
es
h
o
w
g
r
ain
y
t
h
e
i
m
a
g
e
ap
p
ea
r
s
,
th
e
m
o
r
e
g
r
ain
y
,
t
h
e
les
s
th
e
SN
R
.
T
h
e
SNR
is
m
ea
s
u
r
ed
f
r
eq
u
en
tl
y
b
y
ca
lcu
la
tin
g
t
h
e
d
if
f
er
en
ce
i
n
s
ig
n
al
i
n
te
n
s
i
t
y
b
et
w
ee
n
t
h
e
ar
ea
o
f
in
ter
es
t a
n
d
th
e
b
ac
k
g
r
o
u
n
d
.
∑
(
(
)
̂
(
)
)
∑
(
(
)
̂
(
)
)
(
3
)
(
a)
(
b
)
(
c)
(
d
)
(
e)
(
f
)
(
g
)
(
h
)
(
i)
Fig
u
r
e
2
.
Den
o
is
i
n
g
o
u
tp
u
t
s
o
f
MRI
b
r
ain
i
m
a
g
es c
o
r
r
u
p
ted
b
y
A
W
GN
n
o
is
e
o
f
1
0
% p
r
o
b
ab
ilit
y
d
en
s
it
y
(
a)
I
n
p
u
t i
m
ag
e
(
b
)
No
is
y
i
m
a
g
e
-
A
W
GN
(
c)
A
f
ter
w
ie
n
er
f
ilter
(
d
)
A
f
ter
m
ed
ia
n
(
e)
Af
ter
w
a
v
elet
(
f
)
Af
ter
w
a
v
elet
-
m
ed
ian
(
g
)
N
L
M
(
h
)
w
a
v
elet
-
N
L
M
(
i)
P
r
o
p
o
s
ed
w
a
v
elet
b
ased
W
MF
Fig
u
r
e
3
.
P
SNR
,
SNR
a
n
d
MSE
co
m
p
ar
is
o
n
o
f
v
ar
io
u
s
d
en
o
i
s
in
g
s
c
h
e
m
es
f
o
r
No
is
e
(
)
=1
0
,
2
0
1
2
3
4
5
6
7
0
10
20
30
40
50
60
1
-
W
i
e
n
e
r
2
-
M
e
d
i
a
n
3
-
W
a
v
e
l
e
t
4
-
W
a
v
e
l
e
t
-
w
i
e
n
e
r
5
-
N
L
M
6
-
W
a
v
e
l
e
t
-
N
L
M
7
-
w
a
v
e
l
e
t
-
W
M
F
N
o
i
s
e
(
s
i
g
m
a
)
=
1
0
P
S
N
R
S
N
R
1
2
3
4
5
6
7
0
10
20
30
40
50
60
1
-
W
i
e
n
e
r
2
-
M
e
d
i
a
n
3
-
W
a
v
e
l
e
t
4
-
W
a
v
e
l
e
t
-
w
i
e
n
e
r
5
-
N
L
M
6
-
W
a
v
e
l
e
t
-
N
L
M
7
-
w
a
v
e
l
e
t
-
W
M
F
N
o
i
s
e
(
s
i
g
m
a
)
=
2
0
P
S
N
R
S
N
R
1
2
3
4
5
6
7
0
100
200
300
400
500
600
700
800
1
-
W
i
e
n
e
r
2
-
M
e
d
i
a
n
3
-
W
a
v
e
l
e
t
4
-
W
a
v
e
l
e
t
-
w
i
e
n
e
r
5
-
N
L
M
6
-
W
a
v
e
l
e
t
-
N
L
M
7
-
w
a
v
e
l
e
t
-
W
M
F
N
o
i
s
e
(
s
i
g
m
a
)
=
1
0
,
2
0
M
S
E
(
s
i
g
m
a
)
=
1
0
M
S
E
(
s
i
g
m
a
)
=
2
0
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci
I
SS
N:
2502
-
4752
W
a
ve
let
-
B
a
s
ed
W
eig
h
ted
Med
ia
n
F
ilter
fo
r
I
ma
g
e
Den
o
is
in
g
o
f MR
I
B
r
a
in
I
m
a
g
es (
N
.
R
a
ja
la
ksh
mi
)
205
T
ab
le
1
.
Q
u
alitativ
e
a
n
al
y
s
is
-
d
if
f
er
en
t d
en
o
i
s
in
g
s
c
h
e
m
es o
f
m
r
(
m
a
g
n
et
ic
r
eso
n
a
n
ce
)
b
r
ain
i
m
ag
e
co
r
r
u
p
ted
b
y
ad
d
iti
v
e
w
h
i
te
g
a
u
s
s
ian
n
o
i
s
e
D
e
n
o
i
si
n
g
s
c
h
e
me
s
N
o
i
se
(
)
=
1
0
N
o
i
se
(
)
=
2
0
P
S
N
R
S
N
R
M
S
E
P
S
N
R
S
N
R
M
S
E
W
i
e
n
e
r
F
i
l
t
e
r
[
4
]
2
0
.
0
7
1
0
.
6
0
6
3
9
.
9
3
1
9
.
6
4
1
0
.
1
7
7
0
6
.
1
3
M
e
d
i
a
n
f
i
l
t
e
r
[
5
]
2
6
.
4
5
1
1
.
2
1
1
1
7
.
1
3
2
5
.
9
9
1
0
.
8
7
1
1
9
.
1
2
W
a
v
e
l
e
t
-
so
f
t
t
h
r
e
sh
o
l
d
i
n
g
[
6
]
2
7
.
1
4
1
3
.
8
1
9
2
.
3
4
2
5
.
1
6
1
2
.
6
1
9
7
.
7
1
W
a
v
e
l
e
t
b
a
se
d
w
i
e
n
e
r
[
7
]
3
8
.
1
3
1
6
.
7
5
4
1
.
1
5
3
7
.
1
1
1
5
.
6
7
4
2
.
0
2
N
L
M
[
1
0
]
3
3
.
4
2
1
4
.
1
1
6
2
.
1
7
3
1
.
2
3
1
3
.
3
3
6
7
.
6
6
W
a
v
e
l
e
t
B
a
se
d
N
L
M
F
i
l
t
e
r
[
1
1
]
4
3
.
1
2
1
8
.
9
8
1
2
.
3
4
3
9
.
1
1
1
6
.
7
1
1
4
.
4
5
P
r
o
p
o
se
d
w
a
v
e
l
e
t
b
a
se
d
W
M
F
5
6
.
8
9
2
7
.
7
1
1
.
2
2
3
5
1
.
2
1
2
6
.
1
1
3
.
1
1
2
4.
SUM
M
ARY
T
h
e
ex
p
er
i
m
e
n
ts
w
er
e
co
n
d
u
c
ted
o
n
T
2
w
ei
g
h
ted
MRI
d
atasets
,
w
h
ic
h
ar
e
co
r
r
u
p
ted
w
i
t
h
ad
d
iti
v
e
w
h
ite
Ga
u
s
s
ia
n
n
o
is
e,
t
h
e
i
m
a
g
es
ar
e
ac
q
u
ir
ed
u
s
in
g
Sie
m
e
n
s
Ma
g
n
e
to
m
Av
an
to
1
.
5
T
Scan
n
er
.
T
2
w
e
ig
h
ted
MR
b
r
ain
i
m
ag
e
w
it
h
T
R
=
4
0
0
0
m
s
,
T
E
=
1
1
4
m
s
,
5
m
m
th
ic
k
a
n
d
5
9
0
×6
1
2
r
eso
lu
tio
n
.
W
ell
-
k
n
o
w
n
o
b
j
ec
tiv
e
ev
alu
a
tio
n
s
s
u
c
h
as
MSE
,
SNR
an
d
P
SNR
h
av
e
b
ee
n
u
s
ed
f
o
r
m
ea
s
u
r
in
g
th
e
i
m
a
g
e
q
u
alit
y
.
T
h
e
co
m
p
ar
is
o
n
s
o
f
s
e
v
e
n
d
en
o
is
i
n
g
s
c
h
e
m
es
ar
e
tab
u
lated
in
T
ab
le
1
.
I
t
is
o
b
s
er
v
ed
f
r
o
m
t
h
e
T
ab
le
1
,
f
o
r
T
2
w
ei
g
h
ted
M
R
b
r
ain
i
m
a
g
e
s
wav
elet
b
ased
w
eig
h
ted
m
ed
ia
n
f
il
ter
tech
n
iq
u
e
g
iv
e
s
b
etter
r
e
s
u
lt
as
co
m
p
ar
ed
to
o
th
er
d
en
o
is
i
n
g
s
c
h
e
m
es.
Hig
h
er
t
h
e
v
a
lu
e
o
f
P
SN
R
a
n
d
h
i
g
h
er
th
e
v
al
u
e
o
f
SN
R
,
lo
w
er
th
e
v
al
u
e
o
f
MS
E
s
h
o
w
s
th
at
th
e
p
r
o
p
o
s
ed
w
a
v
elet
b
ased
w
e
ig
h
ted
m
ed
ian
f
ilter
p
er
f
o
r
m
s
u
p
er
io
r
th
an
t
h
e
o
th
er
d
en
o
i
s
i
n
g
m
et
h
o
d
s
.
Fro
m
th
e
e
n
h
a
n
ce
d
r
esu
lt
s
,
q
u
a
n
titati
v
el
y
th
e
m
et
h
o
d
p
r
o
d
u
ce
s
g
o
o
d
P
SNR
o
u
tp
u
ts
.
5.
CO
NCLU
SI
O
N
I
n
th
i
s
ar
ticle,
th
e
p
er
f
o
r
m
a
n
c
e
co
m
p
ar
is
o
n
o
f
v
ar
io
u
s
f
i
lter
i
n
g
m
et
h
o
d
s
f
o
r
r
e
m
o
v
i
n
g
ad
d
itiv
e
w
h
ite
Gau
s
s
ia
n
n
o
is
e
f
r
o
m
MR
i
m
ag
es
h
av
e
b
ee
n
d
is
c
u
s
s
ed
.
I
n
th
is
w
o
r
k
T
2
w
e
ig
h
ted
MRI
b
r
ain
i
m
a
g
es
w
er
e
u
s
ed
.
T
h
e
w
a
v
elet
b
ased
w
ei
g
h
ted
m
ed
ia
n
f
ilter
m
et
h
o
d
te
n
d
s
to
p
r
o
d
u
ce
g
o
o
d
d
en
o
is
e
d
i
m
a
g
e
n
o
t
o
n
l
y
i
n
ter
m
s
o
f
v
is
u
al
p
er
ce
p
tio
n
b
u
t
also
in
ter
m
s
o
f
t
h
e
q
u
a
lit
y
m
etr
ics
s
u
ch
a
s
P
SNR
,
SNR
a
n
d
MSE
.
He
n
ce
t
h
e
n
e
w
p
r
o
p
o
s
ed
alg
o
r
ith
m
is
f
o
u
n
d
to
b
e
m
o
r
e
ef
f
icie
n
t
t
h
a
n
th
e
o
th
er
m
et
h
o
d
s
in
M
R
b
r
ain
i
m
ag
e
d
e
n
o
is
i
n
g
p
ar
ticu
lar
l
y
f
o
r
t
h
e
r
e
m
o
v
al
o
f
Gau
s
s
ia
n
n
o
i
s
e.
T
h
u
s
t
h
e
o
b
tain
ed
r
esu
lts
i
n
q
u
al
itati
v
e
an
d
q
u
a
n
titat
iv
e
an
al
y
s
is
s
h
o
w
t
h
at
t
h
is
p
r
o
p
o
s
ed
alg
o
r
ith
m
o
u
tp
er
f
o
r
m
s
t
h
e
o
th
er
m
et
h
o
d
s
b
o
th
v
i
s
u
al
l
y
a
n
d
in
ter
m
s
o
f
P
SNR
.
SNR
,
MSE
.
RE
F
E
R
E
NC
E
S
[1
]
D.L
.
Do
n
o
h
o
.
,
De
n
o
ise
b
y
so
f
tt
h
re
sh
o
ld
i
n
g
,
“
IEE
E
T
ra
n
sa
c
ti
o
n
s
o
n
In
f
o
rm
a
ti
o
n
T
h
e
o
ry
,
4
1
(1
9
9
5
),
p
p
.
6
1
3
-
6
2
7
.
[2
]
R.
G
o
n
z
a
lez
a
n
d
R.
W
o
o
d
s,
“
Dig
it
a
l
Im
a
g
e
P
ro
c
e
ss
in
g
u
sin
g
M
A
TL
A
B,
”
S
e
c
o
n
d
Ed
i
ti
o
n
,
T
HM
(2
0
0
9
).
[3
]
S
.
Ka
ra
a
n
d
F
.
Dirg
e
n
a
li
,
“
A
s
y
ste
m
to
d
iag
n
o
se
a
th
e
ro
sc
lero
sis
v
ia
w
a
v
e
let
tran
s
f
o
r
m
s,
p
rin
c
ip
a
l
c
o
m
p
o
n
e
n
t
a
n
a
ly
sis a
n
d
a
rti
f
icia
l
n
e
u
ra
l
n
e
tw
o
rk
s,”
Exp
e
rt S
y
ste
ms
wit
h
A
p
p
li
c
a
ti
o
n
s
,
v
o
l
.
3
2
,
n
o
.
2
,
p
p
.
6
3
2
-
6
4
0
,
2
0
0
7
.
[4
]
A
sh
o
k
Ku
m
a
r
Na
g
a
w
a
t,
M
a
n
o
j
G
u
p
ta,
P
a
p
e
n
d
ra
K
u
m
a
r
a
n
d
S
u
r
e
sh
Ku
m
a
r,
“
P
e
rf
o
r
m
a
n
c
e
Co
m
p
a
riso
n
o
f
M
e
d
ia
n
a
n
d
W
ien
e
r
F
il
ter i
n
Im
a
g
e
De
-
n
o
isin
g
,
”
In
ter
n
a
ti
o
n
a
l
J
o
u
r
n
a
l
o
f
C
o
mp
u
ter
A
p
p
li
c
a
ti
o
n
s
,
1
2
(2
0
1
0
),
0
9
7
5
-
8
8
8
7
.
[5
]
A
li
No
sra
ti
,
Ha
m
e
d
No
sra
ti
,
M
a
so
u
d
N
o
sra
ti
a
n
d
R
o
n
a
k
Ka
rim
i,
“
A
m
e
th
o
d
f
o
r
d
e
tec
ti
o
n
a
n
d
e
x
trac
ti
o
n
o
f
c
ircu
lar
sh
a
p
e
s f
ro
m
n
o
isy
i
m
a
g
e
s u
sin
g
m
e
d
ian
f
il
ter an
d
CHT
,
”
J
o
u
rn
a
l
o
f
Ame
ric
a
n
S
c
ien
c
e
(2
0
1
1
),
p
p
.
8
4
-
8
8
.
[6
]
N.
Ra
jala
k
sh
m
i
a
n
d
L
a
k
sh
m
iP
ra
b
h
a
,
“
Bra
in
T
u
m
o
r
D
e
tec
ti
o
n
o
f
M
R
I
m
a
g
e
s
B
a
se
d
o
n
Co
lo
r
-
Co
n
v
e
rted
H
y
b
rid
PSO
-
K
-
M
e
a
n
s Clu
ste
ri
n
g
S
e
g
m
e
n
tatio
n
,
”
E
u
ro
p
e
a
n
J
o
u
rn
a
l
o
f
S
c
i
e
n
ti
fi
c
Res
e
a
rc
h
,
v
o
l
.
7
0
,
n
o
.
1
,
p
p
.
5
-
1
4
,
2
0
1
2
.
[7
]
X
iao
f
e
n
g
Ya
n
g
,
Ba
o
w
e
i
F
e
i
,
“
A
w
a
v
e
let
m
u
lt
isc
a
le
d
e
n
o
isin
g
a
lg
o
rit
h
m
f
o
r
m
a
g
n
e
ti
c
re
so
n
a
n
c
e
(M
R)
im
a
g
e
s
,”
M
e
a
s S
c
i
T
e
c
h
n
o
l.
2
0
1
1
F
e
b
1
;
2
2
(2
):
0
2
5
8
0
3
.
[8
]
P
.
Am
u
d
h
a
v
a
ll
i
P
,
“
S
p
a
rse
Ba
se
d
Ro
b
u
st
P
o
in
t
S
e
t
M
a
tch
i
n
g
f
o
r
P
a
rti
a
l
F
a
c
e
R
e
c
o
g
n
it
io
n
,
”
In
tern
a
t
io
n
a
l
Jo
u
r
n
a
l
o
f
A
d
v
a
n
c
e
d
Re
se
a
rc
h
in
M
a
n
a
g
e
m
e
n
t,
A
rc
h
it
e
c
tu
re
,
T
e
c
h
n
o
l
o
g
y
a
n
d
En
g
i
n
e
e
rin
g
(
IJ
AR
M
AT
E)
,
I
S
S
N
2
4
5
4
-
9
7
6
2
(P
ri
n
t),
v
o
l.
2
,
S
p
e
c
ial
Iss
u
e
6
,
M
a
rc
h
2
0
1
6
.
[9
]
Ra
h
u
l
M
a
l
h
o
tra,
M
in
u
S
e
th
i
a
n
d
P
a
rm
in
d
e
r
Ku
m
a
r
L
u
th
ra
,
“
De
n
o
isin
g
,
S
e
g
m
e
n
tatio
n
a
n
d
C
h
a
ra
c
teriz
a
ti
o
n
o
f
Bra
in
T
u
m
o
r
f
ro
m
Dig
it
a
l
M
R
I
m
a
g
e
s,”
Co
mp
u
ter
a
n
d
In
fo
rm
a
t
io
n
S
c
ien
c
e
,
v
o
l.
4
,
n
o
.
6
,
No
v
2
0
1
1
.
[1
0
]
Y.
Ga
l,
A
.
J.
M
e
h
n
e
rt,
A
.
P
.
Bra
d
l
e
y
,
K.
M
c
M
a
h
o
n
a
n
d
D.
Ke
n
n
e
d
y
,
“
De
n
o
isin
g
o
f
d
y
n
a
m
i
c
c
o
n
tras
t
-
e
n
h
a
n
c
e
d
M
R
im
a
g
e
s
u
sin
g
d
y
n
a
m
ic n
o
n
l
o
c
a
l
m
e
a
n
s,”
IEE
E
T
ra
n
s M
e
d
Ima
g
in
g
2
9
:
p
p
.
3
0
2
–
3
1
0
,
2
0
1
0
.
[1
1
]
R.
P
a
v
it
h
ra
,
R.
Ra
m
y
a
,
G
.
A
lai
y
a
r
a
si
,
“
W
a
v
e
let
Ba
s
e
d
No
n
L
o
c
a
l
M
e
a
n
s
A
lg
o
rit
h
m
f
o
r
Eff
ici
e
n
t
De
n
o
isin
g
o
f
M
R
I
Im
a
g
e
s
”
In
ter
n
a
ti
o
n
a
l
J
o
u
rn
a
l
o
f
Ad
v
a
n
c
e
d
Res
e
a
rc
h
i
n
Co
mp
u
te
r
a
n
d
C
o
mm
u
n
ic
a
ti
o
n
En
g
i
n
e
e
rin
g
Vo
l.
4
,
Iss
u
e
2
,
F
e
b
ru
a
ry
2
0
1
5
p
p
.
3
8
8
-
3
9
2
.
[1
2
]
Pa
k
u
t
h
a
riv
u
P
,
S
rin
a
t
h
M
.
V
“
An
a
ly
sis
o
f
F
in
g
e
rp
rin
t
Im
a
g
e
En
h
a
n
c
e
m
e
n
t
Us
in
g
G
a
b
o
r
F
il
terin
g
W
it
h
Diff
e
r
e
n
t
Orie
n
tatio
n
F
iel
d
V
a
lu
e
s
”
I
n
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
E
n
g
in
e
e
rin
g
a
n
d
Co
m
p
u
ter
S
c
ien
c
e
,
V
o
l
.
5
,
No
.
2
,
p
p
.
427
-
4
3
2
,
F
e
b
r
u
a
ry
2
0
1
7
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
SS
N
:
2
5
0
2
-
4752
I
n
d
o
n
esia
n
J
E
lec
E
n
g
&
C
o
m
p
Sci,
Vo
l
.
10
,
No
.
1
,
A
p
r
il
2
0
1
8
:
2
0
1
–
2
0
6
206
[1
3
]
Ch
a
w
k
i
Yo
u
n
e
ss
,
El
A
sn
a
o
u
i
Kh
a
li
d
,
Ou
a
n
a
n
M
o
h
a
m
m
e
d
a
n
d
Ak
sa
s
se
Bra
h
i
m
“
Ne
w
M
e
th
o
d
o
f
Co
n
ten
t
Ba
se
d
Im
a
g
e
R
e
tri
e
v
a
l
b
a
s
e
d
o
n
2
-
D
ES
P
RIT
M
e
th
o
d
a
n
d
t
h
e
G
a
b
o
r
F
il
ters
”
,
T
EL
KOM
NIKA
T
e
lec
o
mm
u
n
ic
a
ti
o
n
,
Co
mp
u
t
in
g
,
E
lec
tro
n
ics
a
n
d
Co
n
tro
l
V
o
l
.
1
5
,
No
.
2
,
A
u
g
u
st
2
0
1
5
,
p
p
.
3
1
3
-
3
2
0
.
DO
I:1
0
.
1
1
5
9
1
/t
e
lk
o
m
n
ik
a
.
v
1
5
i2
.
8
3
7
7
.
[1
4
]
M
a
n
a
r
A
.
M
izh
e
r1
,
M
e
i
C
h
o
o
A
n
g
,
A
h
m
a
d
A
.
M
a
z
h
a
r
“
A
m
e
a
n
in
g
f
u
l
Co
m
p
a
c
t
Ke
y
F
ra
m
e
s
Ex
trac
ti
o
n
i
n
Co
m
p
lex
V
id
e
o
S
h
o
ts”
,
In
d
o
n
e
sia
n
J
o
u
rn
a
l
o
f
El
e
c
trica
l
E
n
g
i
n
e
e
rin
g
a
n
d
C
o
mp
u
ter
S
c
ien
c
e
V
o
l
.
7
,
N
o
.
3
,
S
e
p
tem
b
e
r
2
0
1
7
,
DO
I:
1
0
.
1
1
5
9
1
/i
jee
c
s.v
7
.
i3
.
p
p
8
1
8
-
829
B
I
O
G
RAP
H
I
E
S
O
F
AUTH
O
RS
Dr.
N.Ra
jala
k
sh
m
i
is
a
n
a
ss
o
c
i
a
te
p
ro
f
e
ss
o
r
in
Ka
rp
a
g
a
m
A
c
a
d
e
m
y
o
f
Hig
h
e
r
Ed
u
c
a
ti
o
n
,
b
io
m
e
d
ica
l
d
e
p
a
rtm
e
n
t
Co
im
b
a
to
re
.
S
h
e
re
c
e
iv
e
d
a
B.
E
d
e
g
re
e
in
El
e
c
tro
n
ics
a
n
d
In
stru
m
e
n
tatio
n
En
g
i
n
e
e
rin
g
f
ro
m
Bh
a
ra
th
iar Un
iv
e
rsit
y
in
1
9
9
8
a
n
d
M
.
E.
in
M
e
d
ica
l
El
e
c
tro
n
ics
f
ro
m
A
n
n
a
Un
iv
e
r
sit
y
Ch
e
n
n
a
i
in
2
0
0
2
.
S
h
e
f
in
ish
e
d
h
e
r
d
o
c
to
r
a
l
d
e
g
re
e
in
in
f
o
rm
a
ti
o
n
a
n
d
c
o
m
m
u
n
ica
ti
o
n
sp
e
c
ializin
g
in
m
e
d
ica
l
ima
g
e
p
ro
c
e
ss
in
g
.
S
h
e
h
a
s
o
v
e
r
1
0
y
e
a
rs
o
f
te
a
c
h
in
g
e
x
p
e
rien
c
e
.
S
h
e
h
a
s
p
u
b
li
sh
e
d
m
o
re
th
a
n
1
4
p
a
p
e
rs
in
in
ter
n
a
ti
o
n
a
l
j
o
u
r
n
a
ls
a
n
d
c
o
n
f
e
re
n
c
e
s.
He
r
a
re
a
o
f
in
tere
st i
n
c
lu
d
e
s Im
a
g
e
p
ro
c
e
ss
in
g
,
S
o
f
t
c
o
m
p
u
ti
n
g
,
M
e
d
ica
l
im
a
g
e
a
n
a
l
y
sis.
M
r.
Na
ra
y
a
n
a
n
is
a
n
a
ss
istan
t
p
ro
f
e
s
so
r
in
IRTT
c
o
ll
e
g
e
Ero
d
e
.
H
e
d
id
h
is
b
a
c
h
e
lo
r’s
d
e
g
re
e
a
t
M
a
h
e
n
d
ra
Co
ll
e
g
e
o
f
En
g
in
e
e
r
in
g
,
S
a
lem
in
c
o
m
p
u
ter
sc
ien
c
e
a
n
d
e
n
g
in
e
e
rin
g
.
He
th
e
n
c
o
m
p
lete
d
h
is
p
o
st
g
ra
d
u
a
te
d
e
g
re
e
a
t
G
o
v
e
rn
m
e
n
t
Co
ll
e
g
e
o
f
tec
h
n
o
lo
g
y
,
Co
im
b
a
to
re
.
He
h
a
s
o
v
e
r
1
5
y
e
a
rs
o
f
te
a
c
h
in
g
e
x
p
e
rie
n
c
e
.
His
a
re
a
o
f
in
tere
st
in
c
lu
d
e
s
I
m
a
g
e
p
ro
c
e
ss
in
g
,
T
h
e
o
ry
o
f
c
o
m
p
u
ti
n
g
,
Ne
tw
o
rk
in
g
,
a
n
d
M
e
d
ica
l
im
a
g
e
a
n
a
l
y
sis.
Dr.P
.
Am
u
d
h
a
v
a
ll
i
is
a
n
A
ss
o
c
i
a
te
P
ro
f
e
ss
o
r
in
Ka
rp
a
g
a
m
A
c
a
d
e
m
y
o
f
Hi
g
h
e
r
Ed
u
c
a
ti
o
n
,
De
p
a
rtme
n
t
o
f
Co
m
p
u
ter
S
c
ien
c
e
a
n
d
En
g
in
e
e
rin
g
,
Co
im
b
a
to
re
.
S
h
e
re
c
e
iv
e
d
a
M
C
A
d
e
g
re
e
f
ro
m
Un
iv
e
rsit
y
o
f
M
a
d
ra
s
in
2
0
0
3
a
n
d
M
.
E.
i
n
I
n
f
o
rm
a
ti
o
n
a
n
d
Co
m
m
u
n
ica
ti
o
n
e
n
g
in
e
e
rin
g
f
ro
m
A
n
n
a
Un
iv
e
r
sity
Ch
e
n
n
a
i
i
n
2
0
0
8
.
S
h
e
f
in
ish
e
d
h
e
r
d
o
c
to
ra
l
d
e
g
re
e
in
C
o
m
p
u
ter
S
c
ien
c
e
a
n
d
En
g
i
n
e
e
rin
g
.
S
p
e
c
ializin
g
in
Clo
u
d
C
o
m
p
u
ti
n
g
.
S
h
e
h
a
s
o
v
e
r
1
2
y
e
a
rs
o
f
tea
c
h
in
g
e
x
p
e
rien
c
e
.
S
h
e
h
a
s
p
u
b
li
sh
e
d
m
o
re
th
a
n
1
4
p
a
p
e
rs
in
in
ter
n
a
ti
o
n
a
l
j
o
u
r
n
a
ls
a
n
d
c
o
n
f
e
re
n
c
e
s.
He
r
a
re
a
o
f
in
tere
st i
n
c
lu
d
e
s Clo
u
d
Co
m
p
u
ti
n
g
,
Big
Da
ta,
Im
a
g
e
p
ro
c
e
ss
in
g
a
n
d
S
o
f
t
c
o
m
p
u
ti
n
g
.
Evaluation Warning : The document was created with Spire.PDF for Python.