Bone

segmentation

and

visualization

of

CT

images

for

traumatic

pelvic

injuries

ABSTRACT:

Pelvic

bone

segmentation

is

a

vital

step

in

analyzing

pelvic

CT

images,

which

assists

physicians

with

diagnostic

decision

making

in

cases

of

traumatic

pelvic

injuries.

Due

to

the

limited

resolution

of

the

original

CT

images

and

the

complexity

of

pelvic

structures

and

their

possible

fractures,

automatic

pelvic

bone

segmentation

in

multiple

CT

slices

is

very

difficult.

In

this

study,

an

automatic

pelvic

bone

segmentation

approach

is

proposed

using

the

combination

of

anatomical

knowledge

and

computational

techniques.

It

is

developed

for

solving

the

problem

of

accurate

and

efficient

bone

segmentation

using

multiple

consecutive

pelvic

CT

slices

obtained

from

each

patient.

Our

proposed

segmentation

method

is

able

to

handle

variation

of

bone

shapes

between

slices

there

by

making

it

less

susceptible

to

inter-personal

variability

between

different

patients'

data.

Moreover,

the

designed

training

models

are

validated

using

a

crossvalidation

process

to

demonstrate

the

effectiveness.

The

algorithm's

capability

is

tested

on

a

set

of

CT

data

sets.

Successful

segmentation

results

and

quantitative

evaluations

are

present

to

demonstrate

the

effectiveness

and

robustness

of

proposed

algorithm,

well

suited

for

pelvic

bone

segmentation

purposes.

Traumatic

pelvic

injuries

are

often

high

energy

injuries

that

constitute

a

major

cause

of

death

in

trauma

patients.

Every

year,

traumatic

pelvic

injuries

contribute

to

cases

of

death

and

permanent

disability.

According

to

the

centers

for

disease

control

and

prevention

(CDC),

trauma

injury

kills

more

people

between

the

ages

of

and

than

any

other

disease

or

illness

(CDC

Report,

On

average,

U.S.

workers

die

each

day

from

traumatic

injuries;

simultaneously,

thousands

of

U.S.

workers

visit

emergency

rooms

for

treatment

of

work-related

traumatic

injuries

every

day

(Bureau

of

Labor

Statistics,

More

than

patients

with

pelvic

fractures

were

seen

in

less

than

years

at

the

University

of

Maryland

Shock

Trauma

Center

(Young

et

al.,

and

patients

with

pelvic

bone

fractures

who

are

present

in

shock

have

a

mortality

of

When

combined

with

injuries

in

other

body

regions,

for

example,

the

abdomen,

the

chance

of

mortality

rises

even

higher,

approaching

in

some

cases

Among

different

types

of

trauma

with

a

high

impact

on

the

lives

of

Americans,

traumatic

pelvic

injuries,

caused

mainly

by

sports,

falls

and

motor

vehicle

accidents

contribute

to

a

large

number

of

mortalities

every

year

(Ali

et

al.,

Schiff

et

al.,

Traumatic

pelvic

injuries

and

associated

complications,

such

as

severe

hemorrhage,

multiple

organ

dysfunction

syndrome

(MODS)

and

blood

clots

traveling

to

the

brain

or

lungs,

result

in

the

mortality

rate

from

to

(UMD,

Physicians

treating

traumatic

pelvic

injuries,

especially

pelvic

bone

fractures,

utilize

pelvic

computed

tomography

(CT)

images

as

an

important

resource

for

assessing

the

severity

and

developing

prognosis

of

such

injuries

For

analysis

the

of

pelvic

CT

images

of

injured

patients,

segmentation

of

the

pelvic

bone

from

the

CT

image

is

a

vital

step.

It

can

be

used

to

assess

and

evaluate

ailments,

measure

how

severe

the

injury

is,

provide

quantitative

measurement

of

bone

fracture

and

detect

the

hemorrhage

position

around

the

fractured

bones..

Image

segmentation

is

an

active

research

topic

in

the

field

of

medical

image

processing.

Various

approaches

such

as

thresholding,

region

growing,

atlas

guided,

etc.

are

some

of

the

examples

of

existing

segmentations

methods.

In

recent

years,

new

variations

of

these

segmentation

methods

have

been

proposed

and

utilized

(Gerig

et

al.,

Li

et

al.,

Vicente

et

al.,

Chen

et

al.,

Ladicky

et

al.,

Chen

et

al.

Nevertheless,

each

of

these

segmentation

techniques

has

various

drawbacks,

for

example,

the

shortcoming

of

thresholding

technique

is

that

it

cannot

be

directly

applied

to

multi-channel

images

(Sahoo

et

al.,

Region

growing

is

sensitive

to

the

choice

of

the

initial

seed

(Haralick

and

Shapiro,

Haralick

and

Shapiro,

Some

commonly

used

methods

are

clustering

techniques

such

as

K-means

(Dunn,

Coleman

and

Andrews,

Yao

et

al.,

the

fuzzy

cmean

algorithm

(Lei

and

Sewchand,

and

the

expectationmaximization

algorithm

(Liang

et

al.,

Rajapakse

et

al.,

However,

clustering

algorithms

do

not

directly

incorporate

spatial

modeling

and

can

be

sensitive

to

noise

and

intensity

in

homogeneities.

In

addition,

the

shape

of

the

pelvic

bones

are

quite

complex.

Typical

CT

scans

obtained

from

each

patient

contain

more

than

one

hundred

consecutive

CT

slices.

Each

CT

slice

includes

several

different

bones

with

complicated

and

un-regular

contours.

In

order

to

assist

the

radiologist

diagnosing

pelvic

trauma,

multiple

pelvic

bone

structures

need

to

be

accurately

segmented

for

each

patient's

data.

Moreover,

there

are

some

other

difficulties

such

as

limited

resolution

of

the

original

CT

images

and

possible

fractures

of

pelvic

structures.

CT

images

are

also

susceptible

to

noise,

partial

volume

effects,

and

in-homogeneities.

These

make

the

segmentation

process

much

more

challenging

and

time-consuming.

Furthermore,

due

to

factors

such

as

variations

in

bone

tissues,

complexity

of

pelvic

structures,

and

significantly

different

geometrical

characteristics

of

fractures,

automatic

segmentation

of

pelvic

bone

in

CT

scan

remains

as

challenging

tasks.

Currently,

there

are

no

existing

technologies

with

versatile

capabilities

to

extract

pelvic

bone

structure

and

provide

satisfactory

results.

Hence

developing

an

automated,

efficient

segmentation

algorithm

for

the

assessment

of

pelvic

injuries

can

be

a

vital

tool..

In

this

study,

a

new

cross

validation

based

segmentation

algorithm

is

proposed

to

automatically

extract

multiple-level

bone

structures

using

a

combination

of

anatomical

knowledge

and

computational

techniques.

In

order

to

test

the

accuracy

of

performance

of

the

training

models,

a

cross-validation

process

has

been

designed

to

indentify

how

segmentation

can

be

affected

by

variations

in

training

sets.

In

order

to

quantify

the

segmentation

accuracy,

two

suitable

measures

are

defined

to

evaluate

the

results:

"mean

distance"

and

"mis-segmented

area."

The

results

are

promising,

indicating

that

the

proposed

method

can

successfully

detect

the

ilium,

ischium,

pubis

and

femur

bones;

also

the

quantitative

measurements

demonstrate

the

effectiveness

and

robustness

of

the

proposed

algorithm..

The

rest

of

this

study

is

organized

as

follows.

Proposed

method

for

bone

segmentation

is

explained

in

section

II,

results

are

presented

in

section

III

followed

by

section

IV,

which

presents

conclusions

and

finally

section

V

lists

the

future

work..

II.

METHODS

A.

Pelvic

Bone

Segmentation.

Bone

tissue

segmentation

by

automated

CT

image

processing

can

significantly

reduce

the

time

needed

to

examine

medical

images

and

to

improve

the

accuracy

of

medical

decision-making.

However,

automatic

bone

tissue

segmentation

from

each

CT

image

is

very

challenging

due

to

the

complexity

of

pelvic

structures,

and

variation

in

bone

structure

from

person

to

person.

In

addition,

CT

images

are

susceptible

to

noise,

partial

volume

effects,

and

in-homogeneities.

These

make

the

bone

segmentation

more

challenging

and

time-consuming..

This

study

describes

a

new

cross

validation

based

segmentation

algorithm

in

multi-level

pelvic

CT

scans,

which

can

segment

the

pelvic

bones

from

CT

images

automatically

and

accurately,

as

illustrated

in

Figure

The

proposed

method

consists

of

five

parts:

preprocessing,

edge

detection,

the

best

matching

template

detection,

cross

validation

based

registered

active

shape

model

(RASM)

with

automatic

initialization

and

pelvic

model

reconstruction..

Some

of

the

prior

work

related

to

this

study

focuses

on

the

segmentation

and

fracture

detection

of

pelvic

bones

in

CT

scans

As

shown

in

Figure

the

first

step

of

segmentation

is

preprocessing.

A

multi-stage

preprocessing

technique

is

introduced,

which

includes

surrounding

artifacts

filtering,

morphological

operations,

and

image

enhancement.

This

step

first

removes

the

surrounding

artifacts

present

in

the

original

image,

such

as

CT

table,

cables,

hands,

and

lower

extremities.

High

frequency

speckle

noise

is

also

removed

from

the

images

using

a

median

filter.

Next,

the

image

is

enhanced

to

emphasize

the

features

of

interest-for

example,

the

pelvic

bone

is

enhanced

to

higher

intensity

while

less

emphasis

is

placed

on

soft

tissues

and

other

organs.

Once

image

enhancement

is

conducted

using

brightness

contrast

stretching,

a

series

of

steps

are

applied

for

preliminary

bone

tissue

segmentation,

including

Canny

edge

detection

technique

to

detect

the

edges

of

bone

tissue,

morphological

operations

to

remove

spurious

edges

and

sub-edge

connections

and

removal.

The

obtained

preliminary

segmented

results

are

then

used

for

detecting

the

best

matching

template.

To

design

the

segmentation

process

in

an

automated

fashion,

it

is

performed

by

a

template-based

best

shape

matching

algorithm

(Belongie

et

al.,

For

this

technique,

bone

templates

are

created

through

manual

selection

from

the

visible

human

project

dataset

and

these

templates

are

compared

to

each

CT

slice

in

order

to

determine

the

best

matched

template,

which

allows

the

corresponding

training

shape

models

of

each

best

matched

template

to

be

applied

to

the

detected

edge

for

bone

segmentation.

One

important

consideration

is

that

the

pelvic

bones

in

each

of

the

original

training

images

have

different

sizes,

rotation

angles

and

locations

that

may

lead

to

unstable

and

unreliable

shape

models

for

inaccurate

bone

segmentation.

Hence

to

overcome

this,

a

method

called

registered

active

shape

model

(RASM)

is

used

for

the

final

bone

segmentation

(Wu

et

al.,

By

using

this

method

to

extract

pelvic

bone

tissues,

a

more

robust

training

model

is

developed

as

compared

to

models

developed

using

standard

active

shape

model

(ASM)

algorithms..

ASM

(Cootes

et

al.,

is

a

supervised

learning

technique

that

requires

a

set

of

labeled

training

images.

The

structure

of

interest

in

these

images

is

represented

by

a

series

of

landmarks

manually

placed

along

its

boundaries.

The

statistical

model

of

the

desired

structure

is

formed

by

extracting

the

shape

and

intensity

information

across

the

training

set.

This

model

is

used

to

evaluate

the

possible

deformation

of

each

structure

as

the

algorithm

searches

for

the

same

structure

in

the

test

new

images.

The

first

step

in

constructing

the

shape

model

is

to

determine

a

set

of

n

landmarks,

which

describe

the

structure

of

interest

in

every

training

image.

A

given

object

can

be

represented

as

a

element

vector

x

given

by

x

[x

.,x

n

,y

.y

n

where

(x

i

y

i

provides

the

coordinates

of

landmark

x.

ASM

uses

an

iterative

approach

that

sets

the

model

parameters

b

to

the

mean

shape

of

x,

that

is,

x.

Principle

component

analysis

(PCA)

is

used

to

reduce

the

dimensionality

of

the

dataset.

x

i

can

be

approximated

by:.

where,

P

contains

the

eigenvectors

of

the

PCA

covariance

matrix.

Shape

in

this

frame

is

defined

by

b;

x

is

aligned

with

a

new

set

of

image

points

Y

(representing

the

shape

in

a

new

image).

The

image

points

are

then

projected

into

the

model

space

so

that

the

parameters

in

b

can

be

updated

to

match

them.

The

process

is

repeated

until

there

is

no

significant

change

in

b

between

iterations..

The

entire

RASM

algorithm

includes

two

stages:

training

stage

in

which

registered

training

models

are

created,

and

the

testing

stage

that

includes

automatic

initialization.

During

the

training

stage

of

segmentation,

for

each

diverse

shape

of

the

pelvic

bones

in

different

slices,

distinct

numbers

of

landmarks

are

placed

on

those

boundaries.

Here

shows

different

numbers

of

landmarks

for

each

pelvic

bone:

lumbar

ilium

sacrum

femur

pubis

ischium.

The

numbers

of

the

landmarks

vary

depending

on

the

size

of

each

bone

structure

in

different

training

sample

set.

The

structure

of

interest

in

these

images

is

represented

by

a

series

of

landmarks

manually

placed

along

its

boundaries

by

the

expert.

The

statistical

model

of

the

desired

structure

is

formed

by

extracting

the

shape

and

intensity

information

across

the

training

set.

This

model

is

used

to

evaluate

the

possible

deformation

of

each

structure

as

the

algorithm

searches

for

the

same

structure

in

the

new

test

images.

During

the

test

stage,

the

landmarks

will

move

towards

the

target

position

so

that

the

parameters

of

the

algorithm

will

be

optimized

and

updated

until

there

is

no

significant

change

between

iterations..

ASM

takes

a

statistical

approach

that

requires

a

set

of

labeled

training

images

to

determine

variations

of

the

desired

shape

in

testing

the

new

images.

The

standard

ASM

has

been

widely

used

in

the

recent

years,

but

this

method

is

highly

sensitive

to

initialization.

It

requires

that

the

initial

position

be

correctly

assigned

to

the

training

model

in

order

to

detect

a

target

object

in

the

image.

Then

the

algorithm

can

attempt

to

fit

the

shape

model

to

the

object.

If

the

shape

model

is

not

accurately

placed,

the

standard

ASM

method

often

fails

to

detect

the

target

object..

This

study

addresses

this

shortcoming

using

a

hierarchical

initialization

process

that

composes

image

registration,

extracted

bone

features

as

well

as

prior

edge

detection

results

to

sequentially

place

the

training

models

for

each

individual

object.

This

process

avoids

the

need

for

manually

indicating

the

initial

positions.

This

will

avoid

human

interference

and

reduce

human-caused

errors.

The

algorithm

in

detail

is

described

in

(Wu

et

al.,

In

short,

the

method

makes

use

of

the

sorted

centroids

of

mean

shapes

in

training

models

and

testing

images,

produce

the

paired

up

training

model

and

testing

bone

shapes,

then

assigns

the

initial

position

of

the

mean

shapes

to

the

uppermost

position

of

the

testing

bone..

B.

Considerations

During

Training.

As

ASM

(Cootes

et

al.,

is

a

supervised

learning

method,

we

are

required

to

test

the

reliability

of

the

performance

of

training

models.

A

cross-validation

based

method

is

designed

to

test

how

much

different

training

data

can

affect

the

segmentation

results..

In

traditional

K-fold

cross-validation,

the

original

sample

dataset

is

randomly

divided

into

K

groups.

A

single

group

is

retained

as

the

testing

data

for

validating

the

performance

of

the

training

model,

and

the

remaining

groups

of

data

are

used

to

create

the

training

model.

The

cross-validation

process

is

then

repeated

until

each

of

the

K

subsamples

are

used

once

as

the

validation

data.

Finally,

the

K

results

can

be

averaged

or

combined

to

produce

a

single

estimation

as

the

final

result.

Figure

shows

the

mechanism

of

how

traditional

cross-validation

works..

In

our

designed

cross-validation

method

for

pelvic

bone

models,

the

entire

dataset

patients)

are

randomly

assigned

to

four

distinct

groups.

These

groups

are

not

representative

of

anything

in

specific;

rather

it

is

merely

a

mechanism

to

perform

the

leave

one

group

out

cross

validation

process.

Since

there

are

subjects'

datasets,

each

of

the

four

groups

contains

five

datasets.

Different

from

traditional

cross-validation

method,

within

the

each

group

of

data,

for

each

round,

one

subject

is

selected

from

a

particular

group

for

testing.

The

remaining

subjects

in

the

other

three

groups

are

used

for

creating

three

different

training

models,

which

apply

to

each

selected

testing

subject.

This

process

is

designed

to

avoid

choosing

overlapping

data

for

creating

different

training

models

and

thus

making

them

independent

of

each

other.

Figure

shows

the

mechanism

of

how

our

designed

cross-validation

method

works.

Different

key

pelvic

bone

structures,

including

right

ilium,

left

ilium,

right

femur,

left

femur,

right

pubis

and

left

pubis

are

segmented

to

demonstrate

and

compare

the

performance

of

training

models.

C.

Evaluation

Measures.

These

results

were

evaluated

by

expert

radiologist

as

ground

truth

for

assessment.

Visual

inspection

is

also

used

for

evaluating

the

performance

of

pelvic

bone

segmentation.

The

segmented

bones

are

classified

into

three

categories:

accurate,

fair,

and

unacceptable.

These

categories

were

determined

via

consultation

with

a

trauma

physician,

who

identified

actual

bone

contour

as

the

ground

truth..

The

computed

value

of

mean

distance

can

be

used

to

classify

the

results

in

one

of

three

classes.

The

shapes

represented

with

mean

distances

of

less

than

mm

are

classified

as

"accurate,"

the

shapes

represented

with

mean

distances

between

mm

and

mm

are

classified

as

"fair,"

the

shapes

represented

with

mean

distances

more

than

mm

are

classified

as

"unacceptable."

The

segmentation

result

can

also

be

classified

into

one

of

the

three

classes

using

the

computed

mis-segmented

area

value.

The

shapes

represented

with

mis-segmented

areas

of

less

than

are

classified

as

"accurate,"

the

shapes

represented

with

missegmented

areas

between

and

are

classified

as

"fair,"

the

shapes

represented

with

mis-segmented

areas

more

than

are

classified

as

"unacceptable.".

In

order

to

quantify

the

segmentation

accuracy,

suitable

measure

is

required

to

evaluate

the

segmentation

results.

Hence

specific

for

this

study,

precise

definitions

for

evaluation

criteria

have

been

developed

those

are

useful

and

intuitive

for

evaluation

purposes..

Mean

Distance.

Given

two

surfaces

S

and

S

in

this

study,

S

is

the

ground

truth

of

the

pelvic

bone

surface

as

per

consultation

with

a

trauma

physician;

S

is

the

detected

pelvic

bone

surface

from

our

algorithm..

The

distance

dðp

n

S

Þ

between

two

point

on

surfaces

S

and

S

are

defined

as:.

The

mean

distance

between

surface

S

and

the

surface

S

as:.

where

p

n

denotes

each

landmark/location

on

surface

S

and

p

m

denotes

each

landmark

on

surface

S

L

denotes

the

total

number

of

landmarks

on

the

surface.

During

the

evaluation

stage,

the

ideal

positions

of

landmarks

on

the

gold

standard

surface

of

pelvic

bone

are

determined

by

the

expert

to

measure

the

accuracy

of

the

segmentation.

The

landmarks

are

placed

using

the

expert

knowledge

as

the

ground

truth.

Then,

the

suitable

measures

are

made

between

the

landmarks

in

test

images

and

expected

positions

of

the

ground

truth..

Mis-Segmented

Area.

The

idea

is

to

have

a

measure

that

represents

the

uncommon

area

of

the

segmented

surface

and

gold

standard

surface

of

pelvic

bone.

After

the

bone

structures

have

been

segmented,

the

original

images

are

set

to

the

binary

version.

The

areas

of

the

objects

can

be

determined

in

the

binary

image.

The

area

here

is

defined

as

a

value

corresponding

to

the

total

number

of

pixels

located

within

bone

regions.

Given

two

surfaces

S

and

S

we

define

the

areas

as

A

and

A

A

i

is

the

area

for

each

bone

surface

S

i

.the

Mis-segmented

Area

M

A

of

two

surfaces

S

and

S

can

be

defined

as:.

Pelvic

Bone

Visualization.

Since

pelvic

CT

images

provide

limited

and

local

information

in

each

slice,

pelvic

bone

models

are

very

often

required

to

present

a

full

range

of

the

entire

bone

structure

in

clinical

diagnosis.

The

radiologists

usually

observe

and

analyze

the

patients'

data

with

the

combination

of

both

images

and

models

to

make

accurate

diagnostic

decisions

in

clinical

diagnosis,

which

is

highly

important

for

assessing

the

injury

severity.

After

the

pelvic

bone

from

each

continuous

pelvic

CT

image

is

successfully

segmented,

the

model

is

reconstructed

utilizing

these

extracted

bone

structures.

In

our

work,

the

pelvic

model

can

be

rotated

to

any

angle,

which

will

help

the

physician

comprehensively

observe

the

entire

pelvic

bone

structure

and

detect

any

abnormality

of

the

patient,

such

as

bone

fractures.

Detecting

the

presence

and

extent

of

the

fracture

is

an

important

step

in

assessing

the

severity

of

a

pelvic

injury.

There

might

be

subtle

fractures,

as

fractures

of

the

acetabulum,

hip

displacement

or

presence

of

hemorrhage.

In

addition,

visualization

may

be

used

for

further

validating

the

segmentation

results.

Here

the

isosurface

method

is

applied

to

form

the

pelvic

bone

models

utilizing

the

segmented

pelvic

bones

from

CT

slices.

In

medical

imaging

area,

isosurfaces

is

typically

used

to

demonstrate

regions

of

a

specific

density

in

a

three-dimensional

CT

scan.

This

method

allows

the

visualization

of

organs,

bones,

or

other

structures.

An

isosurface

is

a

surface

that

represents

points

of

an

equal

constant

value

within

a

volume

of

data

distribution.

Isosurfaces

are

often

used

as

data

visualization

methods

and

can

be

drawn

on

the

screen

very

quickly.

After

the

bone

structures

are

extracted

from

continuous

CT

slices,

a

sphere

in

domain

is

set

asfx

g.

A

threshold

is

set

to

in

order

to

create

a

binary

version

of

the

images.

The

pelvic

models

can

later

be

built

based

on

the

binary

version

of

all

CT

slices.

The

pixel

value

of

all

the

nonbone

regions

including

the

background,

soft-tissue

and

all

other

organs

represented

in

CT

images

are

set

to

and

the

bone

regions

are

set

to

An

isofurface

is

therefore

formed

based

on

the

threshold

value

to

separate

the

bone

region

in

each

layer

from

the

background.

The

isosurface

connects

the

points

that

have

the

equal

pixel

value

and

represent

a

model

of

pelvic

bone

structure..

E.

Snake

Method.

In

this

section,

we

compare

the

segmentation

performance

using

the

proposed

method

and

an

automated

seeding

based

Snake

method

(Kass

et

al.,

In

Snake

based

methods,

a

deformable

model

is

matched

to

a

target

in

the

image

by

means

of

energy

minimization.

A

snake

is

initialized

firstly

near

the

target

and

the

snake

gets

refined

iteratively

and

moves

towards

the

salient

contour

(Kass

et

al.,

The

Snake

method

is

often

used

in

image

segmentation.

In

snakes,

a

deformable

model

is

matched

to

a

target

in

the

image

by

means

of

energy

minimization.

A

snake

is

initialized

firstly

near

the

target

and

the

snake

gets

refined

iteratively

and

moves

towards

the

salient

contour..

The

algorithm

for

the

original

Snake

model

was

introduced

by

Kass

et

al.

in

(Kass

et

al.,

The

optimal

snake

shape

and

position

were

defined

based

on

the

solution

of

minimizing

the

sum

of

the

internal

and

external

energies

defined

along

the

snake.

The

snake

S

s

ð

Þ

is

defined

as

a

parameterized

curve:

This

study

provides

a

framework

for

the

pelvic

bone

segmentation

and

pelvic

bone

visualization.

Pelvic

CT

scans

of

patients

are

processed

and

bone

structures

from

fifteen

patients

are

accurately

and

automatically

segmented

using

the

proposed

hierarchical

Figure

Results

of

pelvic

femur

bone

segmentation

using

three

different

training

models

for

a

testing

subject.

[Color

figure

can

be

viewed

in

the

online

issue,

which

is

available

at

wileyonlinelibrary.com.]

Figure

Results

of

pelvic

femur

bone

segmentation

using

three

different

training

models

for

a

testing

subject.

[Color

figure

can

be

viewed

in

the

online

issue,

which

is

available

at

wileyonlinelibrary.com.]

Figure

Results

of

pelvic

pubic

bone

segmentation

using

three

different

training

models

for

a

testing

subject.

[Color

figure

can

be

viewed

in

the

online

issue,

which

is

available

at

wileyonlinelibrary.com.]

Figure

Results

of

pelvic

pubic

bone

segmentation

using

three

different

training

models

for

a

testing

subject.

[Color

figure

can

be

viewed

in

the

online

issue,

which

is

available

at

wileyonlinelibrary.

com.]

approach

which

incorporates

preprocessing,

edge

detection,

shape

matching,

homogeneity

based

image

registration

and

RASM

with

novel

initialization.

Segmentation

results

are

evaluated

using

two

quantitative

measures

and

the

compared

results

show

that

the

proposed

method

performs

better

with

higher

segmentation

accuracy

than

the

standard

ASM.

Also,

compared

results

indicate

that

the

proposed

method

performs

better

than

Snake

algorithm.

Additionally,

computation

time

taken

by

this

method

is

less

than

the

manual

segmentation,

making

it

practically

applicable

in

automated

processing

of

medical

images.

Different

training

models

are

created

to

segment

key

pelvic

bone

structures

and

the

results

show

that

the

selection

of

training

data

does

not

have

a

large

affect

on

the

final

segmentation

performance.

pelvic

bone

models

are

built

based

on

segmentation

results..

V.

FUTURE

WORK

Future

work

includes

the

following

items:

Process

a

larger

database,

with

higher

resolution

CT

images

to

improve

the

performance

of

the

algorithm

in

segmenting

the

bones

and

detecting

the

bone

fracture.

Explore

bone

segmentation

algorithms

based

on

pelvic

CT

images

with

higher

resolution.

Apply

an

appropriate

rule-based

methodology

to

generate

rules

for

outcome

prediction.

This

will

form

a

decision-making

system

to

provide

physicians

with

reliable

recommendations

on

diagnosis

as

well

as

treatment

planning.

A.

Dataset.

The

dataset

has

been

obtained

from

the

Virginia

Commonwealth

University

Medical

Center.

Data

has

been

collected

from

patients

with

traumatic

pelvic

injuries.

Anywhere

between

and

images

are

collected

for

each

patient,

representing

a

variety

of

anatomical

structures

within

the

pelvic

CT

scans,

especially

those

that

display

similar

structures

within

the

group

of

patients.

Axial

CT

images

with

mm

slice

thickness

are

used

for

the

study.

Scans

collected

from

five

patients

are

used

for

training

while

scans

from

the

other

fifteen

patients

are

used

for

testing.

In

order

to

build

appropriate

training

models

for

each

bone

structures,

the

expert's

knowledge

and

experience

is

utilized

to

manually

choose

every

landmark

in

each

training

image.

In

this

study,

it

was

found

five

different

training

images

have

been

sufficient

to

express

the

variation

of

the

couture

for

each

bone

structure.

Also

by

adding

more

than

five

training

images

it

was

found

that

the

performance

of

the

algorithm

became

more

time-consuming

while

not

adding

much

to

the

segmentation

accuracy.

However,

in

our

work,

the

number

of

the

landmarks

placed

into

the

five

training

images

is

more

than

Since

all

these

landmarks

are

placed

manually

by

the

expert,

if

more

training

images

were

to

be

added

then

it

would

take

the

experts/physicians

more

time

to

insert

thousands

more

landmarks

into

the

training

set,

while

not

adding

much

to

the

overall

segmentation

performance.

Hence,

in

order

to

achieve

a

suitable

balance

between

the

running

speed

and

accuracy,

five

training

images

have

been

used

in

this

work.

Figure

shows

the

segmentation

results

based

on

RASM

with

the

automatic

initialization

processing.

The

results

show

that

the

proposed

method

accurately

segments

the

pelvic

bones

(lumbar,

ilium,

sacrum,

femur,

pubis,

ischium).

from

One

Group

Based

Cross

Validation.

Table

shows

the

average

segmentation

performance

for

the

testing

subjects

in

A

using

three

training

models

created

from

B

to

D.

Table

II

presents

the

average

segmentation

performance

for

the

testing

subjects

in

B

using

three

training

models

created

from

A,

C,

and

D.

Table

III

shows

the

average

segmentation

performance

for

the

testing

subjects

in

C

using

three

training

models

created

from

A,

B,

and

D.

Table

IV

presents

the

average

segmentation

performance

for

the

testing

subjects

in

D

using

three

training

models

created

from

A

to

C.

In

the

tables,

MD

denotes

Mean

Distance

between

the

segmented

surface

and

ground

truth

surface..

Figures

and

show

the

example

results

of

left

pelvic

ilium

bone

segmentation

using

three

different

training

models

for

two

testing

subjects.

Figures

and

present

the

example

results

of

left

pelvic

ilium

bone

segmentation

using

three

different

training

models

for

two

testing

subjects.

Figure

shows

the

example

results

of

right

pelvic

femur

bone

segmentation

using

three

different

training

models

for

a

testing

subject.

Figure

shows

the

example

results

of

left

pelvic

femur

bone

segmentation

using

three

different

training

models

for

a

testing

subject.

Figure

shows

the

example

results

of

right

pelvic

pubic

bone

segmentation

using

three

different

training

models

for

a

testing

subject.

Figure

shows

the

results

of

left

pelvic

pubic

bone

segmentation

using

three

different

training

models

for

a

testing

subject..

For

the

segmentation

performance,

of

the

total

testing

subjects

are

classified

as

accurate

and

there

is

slight

difference

among

the

segmentation

results

using

different

training

models.

Based

on

the

entire

performance

of

three

different

training

models

on

key

pelvic

bone

structures

across

testing

subjects,

we

can

conclude

that

the

selection

of

training

data

to

form

training

models

has

slight

influence

to

the

final

segmentation

results.

Also,

each

training

model

performs

well

on

different

testing

subjects

and

most

of

the

segmentation

results

are

classified

as

the

accurate.

The

created

training

models

in

this

study

for

pelvic

bone

segmentation

are

effective

and

reliable.

B I O B I O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I I I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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Integer representations of convex polygon intersection graphs We give the first lower bounds on the grid size needed to represent the intersection graphs of convex polygons. Here each corner of a polygon in the representation must lie on a corner of the grid. We provide a series of geometric constructions showing that for intersection graphs of -translated copies of any fixed parallelogram, grids of size Ω(n 2 ) × Ω(n 2 ) are needed;-translated copies of any other fixed convex polygon, grids of size 2-homothetic copies of any fixed convex polygon, grids of size 2We complement these results by giving a matching upper bound in each case. Hence we settle the complexity of the integer representation problem for these graphs. The upper bounds substantially improve earlier bounds and extend to higher dimensions. If A = {Ai : i ∈ I} is a collection of sets, then the intersection graph of A is a graph G = (I, E) with vertex set I, and an edge ij ∈ E if and only if Ai ∩ Aj = ∅. If A has G as its intersection graph, then we say that A realizes G. In this paper, we consider geometric intersection graphs in which the Ai all represent bounded geometric domains in R d . We are especially interested in the case where d = 2 and the Ai are all similar to a given convex polygon.. Intersection graphs of geometric objects in the plane have a long and rich history, see for instance [21, 22] . These graphs arise for example in the study of wireless communication networks, where the Ai model transmission ranges of devices and one is interested in interference patterns. Intersection graphs have been studied for instance for the following types of geometric objects: segments [14] , continuous curves or 'strings' [9, 12] , unit disks [5, 3] , disks [10] , unit squares [7] , isosceles right triangles or 'semi-squares' [11] , and arbitrary convex sets [17, 18] .. The general study of intersection graphs of convex polygons was initiated by Kratochvíl and Pergel [15] . They proved that the recognition problem for these graphs is NPhard in general and they raised a series of interesting questions for further research. In this paper, we consider the grid size needed to represent the intersection graphs of convex polygons. We end the paper with some problems for future work. The recognition problem for hom(P ) was shown to be NPhard [17, 15] for all convex polygons P , but we are not aware of any result for the recognition problem for trans(P ), other than for the case when P is the unit square. In that case, Breu [2] proved the recognition problem to be NP-hard.. Problem 6.1. Is trans(P )-recognition NP-hard for all convex polygons P ?. As in [23] , our results imply that, provided the base polygon has rational corner points, the recognition problem for trans(P ) and hom(P ) both belong to NP. Thus, the recognition problems for hom(P ) are NP-complete if P is rational, with certificates of NP-ness of bit size O(n 2 ). This, however, does not settle the problem for polygons P that do not have rational corner points. Problem 6.2. Is trans(P )-recognition in NP for all convex polygons P ? Problem 6.3. Is hom(P )-recognition in NP for all convex polygons P ?. Here we think of P as implicit, i.e. not part of the input.. Observe that by Lemma 5.2, our results also apply if P can be transformed into a rational polygon by means of an affine transformation. This is for instance the case for all triangles. A naive approach to try to settle Problems 6.2 and 6.3 might be to find for every polygon P a rational polygon Q such that trans(P ) = trans(Q) resp. hom(P ) = hom(Q). We however strongly suspect that this is not possible. In particular, we suspect that Lemma 5.1 can be turned into an if and only if statement. Another possible direction for further work is the generalization to higher dimensions. As mentioned in Section 5, the proofs of our upper bounds extend trivially to higher dimensions. It is however not clear how much of the lower bound constructions can be salvaged in dimension d ≥ 3.

O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Terminology Tools: State of the Art and Practical Lessons Objectives: As controlled medical terminologies evolve from simple code-name-hierarchy arrangements, into rich, knowledge-based ontologies of medical concepts, increased demands are placed on both the developers and users of the terminologies. In response, researchers have begun developing tools to address their needs. The aims of this article are to review previous work done to develop these tools and then to describe work done at Columbia University and New York Presbyterian Hospital (NYPH). Methods: Researchers working with the Systematized Nomenclature of Medicine (SNOMED), the Unified Medical Language System (UMLS), and NYPH’s Medical Entities Dictionary (MED) have created a wide variety of terminology browsers, editors and servers to facilitate creation, maintenance and use of these terminologies. Results: Although much work has been done, no generally available tools have yet emerged. Consensus on requirement for tool functions, especially terminology servers is emerging. Tools at NYPH have been used successfully to support the integration of clinical applications and the merger of health care institutions. Conclusions: Significant advancement has occurred over the past fifteen years in the development of sophisticated controlled terminologies and the tools to support them. The tool set at NYPH provides a case study to demonstrate one feasible architecture. Concept-oriented terminologies have been proposed and developed by many different researchers over the past decade [1] . All of these works share a knowledge-based approach to term representation. Specifically, they make use of constructs such as semantic networks or conceptual graphs to provide explicit relationships among terms in order to express definitional information about the terms. For example, the term "Aspirin Tablet" might be linked to the term "Medication" via an "is-a" hierarchical relation, to the chemical term "Acetylsalicylic Acid" via a "has-ingredient" relation, and to the drug form term "Tablet" via a "has-dosage-form" relation. Taken in the aggregate, this set of relations serves as a formal definition for "Aspirin Tablet" [2] .. As terminologies evolve from simple code-name-hierarchy arrangements, into rich, knowledge-based representations of medical concepts, increased demands are placed on both the developers and users of the terminologies. In response, researchers have begun developing tools to address their needs. In this paper, I will review published literature on knowledge-based terminology tools (including browsers, editors and servers) and describe the tools in use at Columbia University and New York Presbyterian Hospital. Objectives: As controlled medical terminologies evolve from simple code-name-hierarchy arrangements, into rich, knowledge-based ontologies of medical concepts, increased demands are placed on both the developers and users of the terminologies. In response, researchers have begun developing tools to address their needs. The aims of this article are to review previous work done to develop these tools and then to describe work done at Columbia University and New York Presbyterian Hospital (NYPH). Methods: Researchers working with the Systematized Nomenclature of Medicine (SNOMED), the Unified Medical Language System (UMLS), and NYPH's Medical Entities Dictionary (MED) have created a wide variety of terminology browsers, editors and servers to facilitate creation, maintenance and use of these terminologies. Results: Although much work has been done, no generally available tools have yet emerged. Consensus on requirement for tool functions, especially terminology servers is emerging. Tools at NYPH have been used successfully to support the integration of clinical applications and the merger of health care institutions. Conclusions: Significant advancement has occurred over the past fifteen years in the development of sophisticated controlled terminologies and the tools to support them. The tool set at NYPH provides a case study to demonstrate one feasible architecture. As the sophistication of medical terminologies has evolved, so too have the capabilities of the tools needed for the maintenance and use of the terminologies. A number of commercially available systems are now being offered to support conceptbased terminologies. Our thirteen years of experience at Columbia University and the New York Presbyterian Hospital has provided us with some understanding of the capabilities necessary for successful deployment of controlled terminologies for use in production clinical systems. Among these are the requirements for editors, browsers, and servers.. Editing a terminology is challenging when that terminology includes a rich knowledge base for concept representation. The task is further complicated when it involves the coordination of terms generated by one diverse set of applications and used by another diverse set of applications. We have had some assistance with these tasks through the use of intelligent editing tools that can, for example, assist with automated classification of terms. We have also been able to use batch processes to take updates from a variety of sources and apply them in an efficient and timely manner. However, as the complexity of our task grows with hospital mergers and expansion of clinical systems, the size and complexity of the MED has grown as well. As a result, the single-user editor will no longer be adequate. We are therefore moving to modify our Web-based editor to support distributed editing tasks.. Our terminology server capabilities were developed in direct response to local requirements, before any industry standards were available. According to Chute et al. [31] , the CORBAMed terminology server specification [30] identifies eight mandatory and three optional services. The MED servers in the Unix environment support all of these capabilities.. Chute et al. go on to identify desiderata for clinical terminology servers that would be used for data entry by clinicians. For the most part, our server provides these capabilities, with some notable differences. In particular, we do not support advanced lexical techniques such as word normalization. We have found that rich keyword synonymy serves somewhat the same purpose. We also do not support spelling correction; however, word stemming compensates somewhat for spelling errors.. The "Target Terminology Specification" desideratum is not supported directly; however, we can carry out this function by making use of a more powerful function: class restriction. Class restriction is used to limit the domain over which lexical searches are performed. For example, when attempting to enter a patient problem, if the user types "penicillin", the retrieval can be limited to descendants of the class "Patient Problem", thus retrieving terms like "Penicillin Allergy" while avoiding all of the medication terms that would be retrieved if the restriction was not in force. Class restriction can be used to retrieve terms from target terminologies (to satisfy the desideratum) by limiting the retrieval to descendants of the class that corresponds to the terminology, such as "ICD9-CM Diseases".. For us, terminology services have required not only a library of query facilities but also mechanisms for coordination of editing and distribution functions. A single MED server is not practical for our diverse environment and thus our distributed approach is necessary. Furthermore, we have found that the ability to generate class-based translation tables, in a way that is coordinated with the update process, has greatly simplified the work of application developers who would otherwise have difficulty making their systems work with the MED.. The MED is not intended as a general purpose or standard terminology; the MED content is relevant only for our institution. However, the tools and architecture are not institution-specific (for example, we have successfully used them to browse the UMLS and SNOMED-RT), and the lessons learned are relevant for understanding the challenges involved with maintaining and using knowledge-based terminologies in a wide variety of production clinical information systems. Additional work is needed to create distributed editing capabilities, and to find ways to "MED-enable" commercial clinical information systems being used at our institution.

B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Calculation of gaussian-type quadratures with multiple nodes Abstract-A The presented algorithm for construction of s-orthogonal polynomials with quadratic convergence has a few nice properties. We emphasize the following. The number of nonlinear equations to be solved is small. Ideally, if the starting values are known, our algorithm needs only n -3 nonlinear equations with 4,5,. . , n variables.. l The construction of the Jacobian matrix for the system of nonlinear equations is simplified, and requests only evaluations of 2n integrals for a system of n variables.. l The algorithm can be applied for both bounded and unbounded supports.. As we mentioned before, the Gauss-Christoffel quadrature rule is used for calculation of integrals. The construction of these quadrature rules is not needed for every value of n. In our implementation, we construct a new quadrature rule after n is increased for 8, i.e., we always construct Gaussian rules for n = 8,16,24,. . . .. Finally, for finding all weights, our method uses an upper triangular system of linear equations for the weights associated with each (Gaussian or prescribed) node.

O O O B I I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O


Disaster Prevention Integrated into Commonly Used Web Rendered Systems with GIS Capabilities Abstract: The end of the 20th century brought a remarkable increase in the field of positioning techniques and communications, making them visible and available to the public, which led to an unprecedented interconnectivity. At the same time, disasters are part of our life. Regardless of their nature, measures can be taken, in order to prevent and mitigate their effects, by anticipative preparation or by avoiding the calamity area (if possible). To this end, this paper presents an integrated system, composed of a software component, a hardware component, and a decision-making human element, all having the declared role of diminishing or eliminating human and material losses. The end of the 20th century brought a remarkable increase in the field of positioning techniques and communications, making them visible and available to the public. Among them, one of the most spectacular -which revolutionized the way in which we relate to our planetwas in fact the introduction of GIS (Geographical Information System) positioning technologies on the consumer level, which were actively supported by communities of developers and which transposed this knowledge into a format that the ordinary user can access, starting with the mobile ones and ending with the old and well-known PC. (e.g. Google Earth) [16] . Fire, floods, earthquakes, storms, disasters caused by human negligence can occur and can give rise to irremediable damages, but in some cases, material and human losses can be avoided or, in the worst case, diminished through a set of measures that can be assisted or implemented by means of an information system with spatial capabilities, that collects data from different sources in order to develop a prevention system for such events, that finally has an dissemination role, that can act through deployment of resources, so that the event or the events concerned affect the normal course of life as little as possible [18] .

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Computer simulations of synchrony and oscillations evoked by two coherent inputs Abstract Coherent oscillations have been reported in multiple cortical areas. This study examines the characteristics of output spikes through computer simulations when the neural network model receives periodic/aperiodic spatiotemporal spikes with modulated/constant populational activity from two pathways. Synchronous oscillations which have the same period as the input are observed in response to periodic input patterns regardless of populational activity. The results confirm that the output frequency of synchrony is essentially determined by the period of the repeated input patterns. On the other hand, weak periodic outputs are observed when aperiodic spikes are input with modulated populational activity. In this case, higher firing rates are necessary to input for higher frequency oscillations. The spike-timing-dependent plasticity suppresses the spikes which do not contribute to the synchrony for periodic inputs. This effect corresponds to the experimental reports that learning sharpens the synchrony in the motor cortex. These results suggest that spatiotemporal spike patterns should be entrained on modulated populational activity to transmit oscillatory information effectively in the convergent pathway. Coherent oscillations have been reported in the motor related areas (Ohara et al. 2001; Lee 2003 Lee , 2004 Hatsopoulos et al. 1998; Grammont and Riehle 2003; Kilavik et al. 2009 ) as well as the other cortical areas (Roelfsema et al. 1997; Rodriguez et al. 2004) . For example, 20 to 30 Hz oscillations related to an action have been observed in the premotor cortical neurons (Lebedev and Wise 2000) . In addition, c oscillations related to movement have been observed in the motor cortex recently (Chyne et al. 2008) . c oscillations are thought to be a fundamental process in cortical computation (Fries 2009 ). Precise synchronization among cortical areas suggest information processing such as visuomotor integration (Roelfsema et al. 1997) .. On the other hand, it has been known that the SMA (supplementary motor area) has projections to the primary motor area and neurons in SMA and pre-SMA are activated during the preparatory period of action Kurata and Tanji 1985; Tanji and Shima 1994; Sakai et al. 1996; Cunnington et al. 2002; Isoda and Hikosaka 2007) . Actually, oscillatory activity in the c frequency synchronized immediately before and after the onset of movement in the SMA (Lee 2003) . These results suggest the possibility that SMA neurons play a role in generation of synchronous spikes that trigger the motor cortical spikes with higher firing rates. If it is true, synchronous spikes with low frequent firing rates may be transformed into higher frequent spikes somewhere in the premotor areas including SMA and pre-SMA and the motor cortex.. Anatomically, the premotor areas including SMA and pre-SMA receive reciprocal projections from the parietal cortices through superior longitudinal fasciculus, and the targets of projection are slightly different depending on the area in the parietal cortices (Rizzolatti et al. 1998; Andersen and Buneo 2002) . For example, while superior parietal lobule have reciprocal links with SMA and dorsal premotor region, caudal part of inferior parietal lobule has a major link to the prefrontal cortex (Wise et al. 1997; Petrides and Pandya 2002) . The former pathway is thought to concern visuo-motor information and the latter pathway is to concern the perception and attention. On the other hand, some cortical areas receive projections from the dorsolateral prefrontal and posterior parietal cortices, which include cingulate cortices, superior temporal sulcus, and the premotor cortices such as SMA and pre-SMA (Selemon and Goldman-Rakic 1988; Lu et al. 1994) . Among these complicated pathways, we can suppose at least two distinct pathways between the parietal cortex and the premotor cortex: one is from the parietal to the prefrontal cortices and the other is from the parietal to the premotor cortices. As shown above, the former mainly concerns higher information processing such as attention, and the latter likely concerns visual information such as position and characteristics of an object Pandya 2002, 2007) .. These facts suggest that the interactions between parietal and prefrontal cortices have multiple channels, at least two channels of attention or inattention and characteristic information of an attended object. Thus we think that the information of attended target is shared between the parietal and prefrontal areas through parieto-frontal reciprocal connections. Once the target is locked on, this signal of attention is sent to both premotor and parietal areas. We assume that this state correlates with oscillations between these areas. At the same time, parieto-premotor interactions mainly concerned by the superior parietal cortex provide the premotor cortex with characteristic information of the target. As a result, oscillations and synchronies may occur in the premotor areas through multiple pathways from various areas such as dorsal prefrontal cortex and parietal cortices.. Assuming the two oscillating pathways to the premotor cortex (we call these pathways ''top-down'' and ''bottomup''), we can think that oscillations and synchronies occur by means of the two series of inputs with oscillations (Riehle et al. 1997; Vaadia et al. 1995) . The purpose of this study is to examine the characteristics of output spike patterns when two series (top-down and bottom-up) of periodic spatiotemporal patterns are input to the neural network model in computer simulations. First, let us summarize the results. We clarified the effects of two input factors, i.e. periodic/aperiodic spatiotemporal spikes and modulated/constant populational activity on the periodicity of output spikes, using a simple neural network model. Synchronous oscillations which have the same period as the inputs are observed when the two patterns are input, except when aperiodic spikes with constant populational activity are input. When specific spatiotemporal spikes are periodically input, mean input frequency into each neuron does not need as high as the output frequency even if the populational activity is constant. This is because the period of output oscillation is not decided by the input frequency, but by the period of the input spatiotemporal pattern. In contrast, in the case of aperiodic inputs with modulated populational activity, higher frequent input spikes are needed for higher frequency oscillations in the outputs. For aperiodic input patterns, STDP neutrally suppresses all spikes, because the temporal timings of spikes between pre-and post-synaptic neurons are irregular. In contrast, for periodic patterns, STDP can suppress temporarily generated noisy spikes that have less temporal relations. This result is correspondent with the physiological experiment reports that the synchrony in the motor cortex becomes stronger and more localized in time with improvement of behavioral performance as the learning proceeds (Kilavik et al. 2009 ). Taken together, periodic spatiotemporal patterns will be better than populational modulation as a method of information transmission in oscillatory waves. Since the populational modulation is stable at lower frequency and robust for aperiodic noises, it might be fit for ready state like an idling (Weigenand et al. 2012) . Actually, in the simulations, the input spikes with both properties, i.e. periodic spikes and modulated populational activity, are the best for strong and noiseless autocorrelation. Therefore, the results suggest a good way to transmit oscillatory information in the convergent pathway as follows: spatiotemporal spike patterns should be entrained on modulated populational activity. Then it must be natural that both of modulated waves and spatiotemporal spikes are observed in the brain. The direct evidence of spatiotemporal patterns are few, but a transient synchrony such as ''unitary event'' has been reported (Kilavik et al. 2009; Grammont and Riehle 2003) . Some transient synchronies organized by cell assemblies (Singer 1999 (Singer , 2009 ) may contribute to a transient spatiotemporal pattern. These transient or short-term synchronies may be observed in longer time scale as higher frequency wave such as a b or c wave.. According to the previous numerical study, it is known that a synchronous spike occurs only if the learned spatiotemporal pattern is input after synaptic learning (Hosaka et al. 2008) . The synchronous signal can activate the primary motor cortex, which will induce the movement. If the model learns the transformation from a spatiotemporal to a synchronous pattern, both of sensory and top-down signals are necessary to synchronous spikes. Concretely, when the sensory signals are input without the top-down signal or the top-down signal is on without the sensory signal, synchronous spikes seldom occur. Therefore, when the top-down signal is received, we can consider that this circuit waiting for the specific sensory input is in the ready state to trigger the movement.. The generated synchrony (or oscillations) may contribute to the functions such as making links between areas (Fries 2009) , control the flow of information (Lee 2004) , or integration of sensory and other information (Roelfsema et al. 1997) . The results suggest that each frequency band such as a, b, c differs in the generation mechanism and the role of information processing in the brain. This section explains the details of the neural network model. First, the model neuron which composes the neural network is clarified, then the network structure and the synaptic plasticity are described.. At macroscopic level, the cortex consists of six layers and there exist interactions between the layers. In this study, to focus on the activities of the neurons, where two coherent oscillations are input to convergently, our simplified neural model assumed to be in the same column that receives signals from different cortical areas. In our model, one unit of the model synapse represents an average over hundreds of synapses for simplicity. Thus the standard deviation of synaptic weight will decrease to 10 -1 . Although each synaptic weight is unreliable device (Freche et al. 2011) , we approximate each unit weight as a mean value. Thus, the present study focuses on, so to speak, mesoscopic phenomena of oscillatory neuronal activities (Foster et al. 2008; Guo 2011; Schütt et al. 2012 ).

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Implications of E-learning systems and self-efficiency on students outcomes: a model approach Background: This paper presents a model approach to examine the relationships among e-learning systems, self-efficacy, and students' apparent learning results for university online courses. Abundant e-learning empirical research points out those superior e-learning outcomes are one of the critical objectives of e-learning research. Our path analytical model suggests that of these six variables I hypothesized, all of them are useful predictor of elearning outcomes, except the following three unsupported hypotheses. The paths from system quality and information quality to user satisfaction, system use to user satisfaction, and user satisfaction to e-learning outcomes were significant as hypothesized by the DeLone-McLean model. On the other hand, the paths from system quality to system use, system use to e-learning outcome, and self-efficacy to user satisfaction were not significant. This negative finding may be explained by the mandatory nature of the e-learning system. This is in accordance with the findings of the study of Livari [45] , which tested the DM model in a mandatory city government information system context. System use is the pivot of the DM model. System use, either actual or perceived, is one of the most frequently reported and the most objective measure of MIS success or the MIS success measure of choice in MIS empirical research [3] in a voluntary IS use context. The DM model has been empirically tested using structural equation modeling in a quasi-voluntary IS use context [46] and in a mandatory information system context [45] . Nevertheless, the usage of information and systems, as repeatedly pointed out by DeLone and McLean [3] , is only relevant when such use is voluntary. Needless to say, e-learning systems are mandatory systems. Regardless of the quality of the e-learning management system, e-learners must use the system. We suggest that future e-learning empirical studies exclude "system use" construct in the model. An important goal of e-learning systems is to deliver instructions that can produce equal or better outcomes than face-to-face learning systems. To achieve the goal, an increasing number of empirical studies have been conducted over the past decades to address the issue of what antecedent variables affect students' satisfaction and learning outcomes and to examine potential predictors of e-learning outcomes [1, 2] . A primary theme of e-learning systems research has been empirical studies of the effects of information technology, instructional strategies, and psychological processes of students and instructors on the student satisfaction and e-learning outcomes in university online education.. The research model we developed is a blend of a management information systems (MIS) success model [3] , a conceptual model of Piccoli et al., [4] , and an e-learning success model of Holsapple and Lee-Post [5] . Based on the review of 180 empirical studies, DeLone and McLean presented a more integrated view of the concept of information systems (IS) success and formulated a more comprehensive model of IS success. Their IS success model identified six constructs that are interrelated and interdependent: system quality, information quality, use, user satisfaction, individual impact, and organizational impact. DeLone and McLean's [6] model is further extended and adapted to e-learning settings by many e-learning systems research. Holsapple and Lee-Post [5] adapted the DeLone and McLean model to propose e-learning success model ( Figure 1 ). The proposed e-learning success model consists of three antecedents constructs (system quality, information quality, service quality) and two intervening constructs (system use and user satisfaction) and system outcome measuring academic success and systems efficiency and effectiveness ( Figure 2 ). The primary objective of this study is to investigate the determinants of students' perceived learning outcomes and satisfaction in university online education using e-learning systems. Using the extant literature, we begin by introducing and discussing a research model illustrating variables affecting e-learning systems outcomes and user satisfaction. We follow this with a description of the cross-sectional survey that was used to collect data and the results from a path analysis model. In the final section, we outline the implications of the results for higher educational institutions.

O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Orthogonal sequences based multi-CFO estimation and semi-blind ICA based equalization for multiuser comp systems Abstract. We propose a low-complexity carrier frequency offset (CFO) estimation approach and an independent component analysis (ICA) based semi-blind equalization structure for the orthogonal frequency division multiplexing (OFDM) based multiuser coordinated multi-point (CoMP) systems. A group of orthogonal sequences are employed to enable separation and simultaneous estimation of multiple CFOs at base stations, dividing a complex multi-dimensional search into series of low-complexity mono-dimensional searches. Then, a small number of pilot symbols attached to the source data, are used to resolve the ambiguity problem in the ICA equalized signals. It has a low complexity, as the permutation and quadrature ambiguity can be eliminated simultaneously, rather than sequentially by the previous precoding aided method. Simulation results show that the proposed semi-blind equalization structure has a bit error rate (BER) performance close to the ideal cases of the zero forcing (ZF) and minimum mean square error (MMSE) based equalizations with perfect channel state information (CSI) and no CFO, and also significantly outperforms the constant amplitude zero autocorrelation (CAZAC) sequences based CFO estimation method.Keywords: carrier frequency offset (CFO), coordinated multi-point (CoMP), orthogonal frequency division multiplexing (OFDM), independent component analysis (ICA). Inter-cell interference is a major bottleneck for achieving a very high data rate in wireless communications [1] - [2] . Many methods have been considered to manage interference in wireless networks. Previously, adjacent cells are operating on different frequencies to effectively reduce the inter-cell interference. However, as the demand for the high mobile data rates is exponentially growing, higher spectral efficiency is driven [3] - [4] . One of the best ways to manage interference and to improve the spectral efficiency in wireless networks, is to allow base stations to connect to each other through a backhaul link, where they could exchange the messages and jointly process the received multiple users' signals on the same frequency band. This technology is called the uplink co-ordinated multi-point reception (CoMP) [2] , which has received much attention recently, and has been adopted by the 3GPP LTE-Advanced standard [2] as a key technology to reduce inter-cell interference and to improve the service coverage. CoMP can explore the interferences between the cells, which is different from other existing methods by treating them as noise [3] - [6] . Therefore, CoMP enables vast spectral efficiency in current wireless communication systems structure. We have proposed a low-complexity orthogonal sequences based multi-CFO estimation approach, and a pilot aided and ICA based semi-blind equalization structure for multiuser-CoMP OFDM systems. Based on the properties of the orthogonal sequences extracted from the Hadamard matrix, the multiple CFOs are separated and estimated simultaneously. The multi-dimensional multi-CFO search is divided into a serials of one-dimensional searches with low complexity and high accuracy. By utilizing the cross-correlation between the original and equalized pilot symbols, the permutation and phase ambiguity due to the ICA model are resolved simultaneously, without increasing the computational complexity. The proposed pilot-aided semi-blind equalization structure does not provide a BER performance closed to the ideal case with perfect CSI and no CFO, but also has a much better MSE performance than the CAZAC sequence based CFO estimation scheme [10] . The proposed CFO estimation has a robust performance against the increase of the number of users, and its MSE performance becomes better when the CFO value is closer to zero. The proposed pilot aided ICA based equalization is more robust against channel variations, than the precoding aided semi-blind structure. Channel estimation and multiuser detection play an important role in multiuser CoMP systems. Two types of approaches, namely training based methods [7] - [9] and blind methods [10] - [15] , are commonly used in wireless communications to estimate the channel state information (CSI) and to recover the transmit signals. On the one hand, transmitting training signals or pilots reduces the spectral efficiency, especially in wireless communication systems with very scarce bandwidth resource. Most of previous works related to the training sequence bring considerably extra bandwidth. [7] and [8] propose an adaptive MIMO single carrier frequency-domain equalization. However, their training overhead are up to 13% and 10%, respectively. In [9] , the training sequences are used to initialize the equalizer coefficients to combat the Doppler shift. Although the training overhead is as low as 0.05%, the actual training sequences are up to 160 for one transmit antenna at each time of initialization. On the other hand, the pure blind methods increase the computational complexity, particularly at the receiver. If little prior knowledge or information is available at the receiver, the semi-blind approaches [16] - [18] can be employed as a good trade-off relationship between the number of training symbols and the computational complexity.. Blind source separation method can obtain the channel state information, without introducing extra training sequences. The equalizer coefficient can be directly obtained from the statistics of the received data, with the no need to use extra bandwidth and spectral. In fact, blind source separation method is divided into second order statistics (SOS) [12] - [15] and higher order statistics (HOS) [9] , [16] - [18] . The Higher Order Statistic is more against the Gaussian noise, as the fourth or higher cumulants of Gaussian noise are equal to zero. In [12] - [13] , a non-redundant linear precoding is applied to the source data, and the blind channel estimation is obtained, by exploring the signal covariance matrix via the precoding, based on the second order statistics of the received signals at the receiver. This work is extended to the MIMO case in [14] and [15] . However, all the papers above are based on the SOS only, which is sensitive to the Gaussian noise.. Independent component analysis [10] - [11] , is one of the higher order statistics (HOS) based blind source separation approaches, which can estimate the data directly from the statistics of the received signals, by maximizing the nonGaussianity of the received signals, without knowing the CSI, as long as the received signal corresponds to a linear mixture. However, as a common drawback of blind approaches, phase, quadrature and permutation ambiguity still exist in the ICA based approaches. In [9] , ICA was applied on all subcarriers, and the statistical correlation between subcarriers is used to resolve the frequency dependent permutation. However, this approaches propagate bit errors and ambiguity errors across subcarriers. In [16] and [17] , a large number of training sequences are used to eliminate the ambiguity, which, however, reduces the spectral efficiency of the system. In [18] , the precoding is employed at the transmitter, by superimposing the reference data to the source data. At the receiver, the permutation and phase ambiguity is eliminated, by using the correlation property between the reference symbols, without consuming extra spectral. However, it is very sensitive to the precoding constant and the frame length, and has a good bit error rate (BER) performance only when data frame size is large enough.. Orthogonal frequency division multiplexing (OFDM) [19] - [28] is an effective technology to combat frequency selective fading, and has been adopted in the fourth generation (4G) wireless communication systems. One of the main drawbacks of OFDM systems is their sensitivity to the carrier frequency offset (CFO), due to the imperfect local oscillators between transmitter and receiver, destroying the orthogonality among the subcarriers, and inducing the additional intercarrier interference (ICI). In the OFDM based multiuser-CoMP system, multiple CFOs exist between mobile terminals and base stations, introducing multiple access interference (MAI). The optimum maximum likelihood (ML) based CFO estimation method is proposed in [19] . However, it can only be used in single CFO systems, and its complexity is prohibitive in real systems with multiple CFOs, due to the exhaustive multi-dimensional search. In [20] , a sub-optimal CFO estimation, using constant amplitude zero autocorrelation (CAZAC) sequences, is proposed. However, there is a mutual interference due to multiple CFOs at each base station, resulting in a biased CFO estimation. Space time block codes transmission with multiple CFOs are proposed in cooperative wireless communication in [21] . However, it does not consider the multi-CFO estimation and the channel estimation. An optimum ML based estimator is proposed for downlink multiuser CoMP systems in [22] . However, its complexity is very high. An approach of approximation for ICI matrix based on the Hadamard sequence is proposed in [23] , However it is a biased CFO estimation method. Moreover, in [24] - [28] , they require transmitting a large number of training sequences for accurate CFO estimation.

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