Automatic Colon Segmentation Using Isolated-Connected Threshold

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6 Νοε 2013 (πριν από 3 χρόνια και 7 μήνες)

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Automatic

C
olon
S
egmentation
U
sing
I
solated
-
C
onnected
T
hreshold


Ku
-
Yaw Chang
*
,
Hao
-
Han Zhang
*
,
Shao
-
Jer Chen
#,¶
, Lih
-
Shyang Chen
§
,
Jia
-
Hong Chen
*

*

De
partment of Computer
Science and Information Engineering, Da
-
Yeh University, Changhua, Taiwan, R.O.C.

e
-
mail:
canseco@mail.dyu.edu.tw
,

gama02174609@hotmail.com
,
xzone0826@gmail.com

#

Department of Radiology, Buddhist Dalin Tzu Chi General Hospital, Chiayi, Taiwan, R.O.C.



School of Medicine, Buddhist Tzu Chi University, Hualien, Taiwan, R.O.C
.

e
-
mail:
shaojer.chen@msa.hinet.net

§
Department of Electrical Engineering, National Cheng
-
Kung University, Tainan, Taiwan, R.O.C
.

e
-
mail: chens@mail.ncku.edu.tw


Abstract


V
irtual

colonoscopy

(VC)

is a safe and fast medical
imaging procedure to screen the colon for polyps. And it has
become very popular recently. Colon segmentation is a
necessary and important step of such an examination
procedure. In this paper, an automatic colon segmentation
met
hod is proposed.
T
he fluid inside the colon is first identified
and removed based on its characteristic of horizontal surface.

A simple 3D region growing algorithm is applied to obtain
initial segmentation of air, which is served as the basis of the
ensui
ng automatic locating object and background seeds. Then
the isolated
-
connected threshold algorithm, together with the
above seeds, is applied to obtain the final results. The colon
can
be
obtained by applying morphological operations to the
segmentation re
sults of air. Our proposed algorithm can
automatically segment different substances based on the
isolated
-
connected threshold. It allows the user to modify the
segmentation results interactively by providing more object or
background seeds.

Keywords



virt
ual colonoscopy, colon segmentatioin,
isolated
-
connected thresholding,
image processing, region
growing

I.


I
NTRODUCTION

Colorectal cancer has been the third leading cause of
cancer deaths in Taiwan for the last decade[
1
]. Even though
the exact cause of most
colorectal cancers is still not known,
it is possible to prevent many cases. Prevention and early
detection are possible because most colorectal cancers
develop from polyps


precancerous tissue growths. Early
screening can help find polyps, which can be e
asily removed,
thereby lowering a person’s cancer risk.

For years, optical colonoscopy (OC) has been the gold
standard for colorectal cancer screening. However, OC is
often regarded as an invasive, highly uncomfortable and
expensive technique, and thus becomes an undesirable
procedure patients are reluctant to
undergo. An alternative
diagnostic procedure to OC is virtual colonoscopy(VC),
which is the process of combining multi
-
slice CT images and
advanced visualization techniques to allow radiologists to
interactively view, manipulate and examine the interior of

the colon and even detect polyps or tumors. VC is a quick
and non
-
invasive procedure that does not require sedation or
anesthesia. It eliminates physical discomfort and associated
risks, such as bleeding and perforation of the colon wall.

Colon segmentati
on is an essential step of creating a 3D
colon model for VC. To become a clinically viable screening
method, VC needs automatic segmentation methods to
reduce the amount of user interaction required to generate
accurate models. Several sophisticated semi
-

and fully
automated segmentation algorithms for the colon have been
reported in the literature, which are commonly based on
region
-

or volume
-
growing techniques. Wyatt et al. [2] used
a region
-
growing technique to segment the colon, in which
the automated
selection of
seed voxels was based on the
distance transform. Although the colon was segmented
satisfactorily, a small bowel or stomach was present in a
majority of the segmentation results. Chen et al. [3] used
vector quantization to label voxels. The col
onic walls were
segmented by region growing based on the labeled voxels. 6
of 21 datasets cannot be segmented satisfactorily. Masutani
et al. [4] used an anatomy
-
oriented technique to remove
anatomic structures surrounding the colon before
thresholding of
the colon wall. This approach was later
combined with a region
-
growing
-
based technique for final
segmentation. However, 10%
-
15% of the results consisted of
extracolonic objects, such as small bowel and stomach.

Three major problems makes colon segmentation

a
difficult task to automate [2][5]. First, the colon in a CT
image often consists of an uncertain number of disconnected
regions due to its complex 3D winding structure. Second, the
colon is not the only air
-
filled structure in the abdomen.
Lower portion
s of the lung are often present, and portions of
the small bowel and stomach may also be partially filled with
air. It is incorrect to claim that all air voxels are part of the
colon. Third, obstructions in the colon itself complicate
automated segmentatio
n, prevent a continuous colon
segment, and require the use of multiple manually placed
seed points. Possible obstructions include fluid, very large
lesions, and residual feces. The existence of such
obstructions creates the partial
-
volume
-
effect(PVE)[6],
w
hich emerges on the boundary between low and high
density regions during imaging.

In clinical
application
, the physician

may

expect to
have
an opportunity to modify the results for verification purpose
s,
both interactively and intuitively.
In this paper, a
n interactive

colon
segmentation
algorithm

is proposed
.
The algorithm is
essentially an isolated
-
connected threshold operation with
seed points determined automatically, not interactively. The
physician is then allowed to examine and modify the result
intu
itively

-

simply

click the want
ed

or unwanted regions.


The remainder of this paper is organized as follows.

Section 2 describes the segmentation procedure
using
isolated
-
connected threshold
.

And
several
e
xperimental
results

are given in Section 3
, followed by a conclusion in

Section
4
.

II.

M
ETHODOLOGY

T
h
e proposed
colon
segmentation

method
is
bas
ed on
identifying the

colonic interior
, i.e. the
air

inside the colon,
first. And based on th
is

interior

identification
, the colon can
consequently
be segmented
out
.

The
overall
segmentation

procedure consists of
the following
steps: pre
-
processing,
preliminary

segmentation
, isolated
-
connected threshold

and
colon identification.

A.

Pre
-
processing

In clinical practice
, it is quite difficult to remove the fluid
completely during bowel
preparation.
Th
e fluid can
separate
the colon
passage
into
several

disconnected

volumes
,
especially when the segmentation proceeds based on the air
channel.

Thus, t
he pre
-
processing procedure
aims at
removing fluid inside the colon

digitally
.

1.

Air and Fluid
Identification

A proper threshold value is selected based on the gray
-
level histogram of the whole volumetric abdominal CT
dataset to differentiate the air and fluid inside the colon.
An
example of the
result

is
illustrated in Fig.
1
.

The id
entified
fluid components will be removed from the image



labeling
fluid voxels as air.

2.

PVE Voxels Removal


In this research, we focus on the PVE on the air and fluid
boundary, where the intensities of these voxels do not belong
to either the air or the fluid ranges. When CT images are
acquired, the fluid inside the colon gathers into some
concave parts of the c
olon and has a horizontal surface due
to the gravity force.

W
e

use the vertical filter

technique
to remove PVE
voxels
[6]
. T
he colon passage become connected
, as
illustrated in Fig.
2,

and
the
subsequent 3D region growing is
therefore viable.

B.

Preliminary

Segmentation

The
preliminary segmentation is achieved by applying
the 3D region growing.

This preliminary
result

is used to
help determine s
eed
sets

automatically

required in

S
tep

C
.

1.

Seed Point
Placement

The

seed point
for

3D region growing

can be localized
without human involvement, based on the fact that

the last
slice of the whole volumetric data contain
s

the rectum
.
Otsu’s
threshold
scheme

and a logical operation between
images are performed
to extract the hollow area of the
Fig.
1
.

(a) a clipped colon image

(b) air component


(c) fluid component

Fig. 2
.

Segmentation results (a)
before

(b)
after
removing

PVE voxels

Fig. 3
.

A
utomated

seed point selection

based on
anatomical

knowledge

Fig.
4

Utilize the bounding box to determine
the
first
set of seed points.

Fig.
5
.

Search for the second set of seed points.

The search
area

is limited to the dash box for the left
-
top quadrant.

tube
[7][8]
. The center pixel
of

the segmented region
is

used
as a seed voxel for 3D region growing
, as shown in Fig. 3
.

2.

3D Region Growing

Since th
e init
ial seed poi
n
t is automatically

located and
the PVE voxels are removed, a 3D region growing algorithm
can be applied to the whole colon dataset for air
segmentation. This well
-
know
n

algorithm
is

described in
many texts and has several variations

[
9]
.

We use a similarity measure to determine if voxels
belong to the region. Given an initial seed point, the
neighborhood is examined to determine if any connected
voxels have similar characteristics to the seed point. Those
voxels in the neighborhood with a
sufficient similarity
measure are collected. Since the air component has a certain
gray
-
level value, i.e.
-
1000 HU, a more strict similarity
measure is preferred. This process continues by removing a
voxel from the collected neighbors, adding it to the reg
ion,
and examining its neighbors for placement in the collection.
The algorithm stops when no more voxels remain in the
collected neighbor set.

3.

Seed
Set
s Placement

Based on the result of 3D region growing, two sets of
seed points
are collected
automaticall
y
for
later use.

For each region in
a
2D image, all pixels
lies on its
bounding box are
labeled

as the first set of seed
s
, as shown
in Fig. 4.

For the second set of seed
s
, a search process is conducted
for each
quadrant of a
region, which starts from the center of
the
bounding box. The search
area for each quadrant
is as big
as the bounding box, as illustrated in Fig. 5.
During the
search process, o
nce a pixel
, whose value falls between the
air and colon, is found, the pixel i
s added to the second
set of
seed
s
, and the search stops.

Therefore, for each region, there
are four pixels selected as the seeds, one for each quadrant.

C.

Isolated
-
Connect
e
d Threshold

The
isolated
-
connected threshold
uses a binary search to
adjust
lower
and upper values
, trying to find the optimal
threshold

to
ensure that all of the first seeds are contained in
the resulting segmentation and all of the second seeds are not
contained in the segmentation.

[10]
.

Since two sets of seed

point
s are obtained in

S
tep

B
-
3,
the

isolated
-
connected threshold algorithm can be executed

without user involvement.
The
user

can
examine

the
segmentation
results with little effort.

And if necessary, the
user can modify segmentation results in an intuitive way
-

simply using
mouse clicking to label
pixels as

wanted


or

unwanted


to change the two sets of seed points. The
isolated
-
connected threshold

is then performed again to
reflect such a change of seed points.

D.

Colon
Segmentation

The segmentation results from the previous step actually
are

composed of
air and removed
fluid

components, not the
colon itself.

A

3D binary morph
ological operation
-

dilation
is

applied to
such results

to obtain the colon itself
[
11
]
. The
greater size of the dilation operator
is
,
the more

tissue voxels
contained
beneath the
colon
surface
, which
make
s

it possible
for radiologists to explore not only

the colon surface, but
also its underneath tissues

III.

E
XPERIMENTAL
R
ESULTS

The proposed algorithm was implemented based on the
Insight Segmentation and Registration Toolkit (ITK)
, which
provides extensive C++ classes

for image analysis
[10].

We
applied our me
thod to s
everal
cases of virtual colonoscopy.
Examples of CT images
and their segmentation results are
illustrated in Fig. 6.

In Fig. 6, images in column (a) are clips from the original
CT images for better visualization effects. Their preliminary
results,

i.e. after applying a 3D region growing algorithm

with a gray
-
level range of
-
1024 to
-
475, are shown in
column (b). It is easy to see that the results are unsatisfactory.
Based on
results
in column (b), we apply isolated
-
connected
threshold algorithm to
further
improve

the results, as
Fig.
6.

(a) original images; (b)
preliminary segmentation
results; (c) final segmentation results.


Air/Fluid components are put in red color.

illustrated in column (c).

It is approved by the radiologist
that images in

column

(c) h
ave better segmentation results.

IV.

C
ONCLUSION

In this paper, we have proposed
an automatic

c
olon
s
egmentation
algorithm. After removing
fluid inside the
colon digitally in the pre
-
process stage, the threshold
-
based
3D region growing is applied to obtain preliminary results.
And the isolated
-
connected threshold is also applied to get
better results thereafter. The selection of seed points f
or both
3D region
-
growing and isolated
-
connected threshold can be
carried out automa
t
ically
. Thus, the radiologist can easily
view the segmentation results with little effort.

Besides the automatic processing flow, our

method

also

allows the
radiologist

to

refine the results in an intuitive
approach at the final stage. This interaction is quite helpful in
clinical use.

Our future work
s

include

a quantitative evaluation
, and

a
clinical

validation

of the proposed algorithm.

ACKNOWLEDGMENT

This work was suppor
ted by the grant contract
NSC 99
-
2221
-
E
-
212
-
012
-
MY3

from National Science Council,
Taiwan
, R.O.C.

R
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