{contact:faraggcvip.uofl.edu}

2007 IEEE International Symposium
on Signal Processing and Information Technology
EXPERIMENTS ON SENSITIVITY OF TEMPLATE MATCHING FOR LUNG
NODULE DETECTION IN LOW DOSE CT SCANS
Shireen Y Elhabian1, Hossam Abd EL Munim1, Salwa Elshazly', AlyA.Farag1, and Mohamed Aboelghar2
'Computer Vision and Image Processing Laboratory, Univ. of Louisville, Louisville, KY, 40292
2Urology and Nephrology Center Mansoura University, Mansoura, Egypt
{contact:faraggcvip.uofl.edu}
ABSTRACT
Template matching is a common approach for detection of
lung nodules from CT scans. Templates may take different
shapes, size and intensity distribution. The process of nodule
detection is essentially two steps: isolation of candidate nodules, and elimination of false positive nodules. The processes
of outlining the detected nodules and their classification
(i.e., assigning pathology for each nodule) complete the
CAD system for early detection of lung nodules. This paper
is concerned with the template design and evaluating the
effectiveness of the first step in the nodule detection process.
The paper will neither address the problem of reducing
false positives nor would it deal with nodule segmentation
and classification. Only parametric templates are considered.
Modeling the gray scale distribution for the templates is
based on the prior knowledge of typical nodules extracted
by radiologists. The effectiveness of the template matching
is investigated by cross validation with respect to the ground
truth and is described by hit rate curves indicating the probability of detection as function of shape, size and orientation,
if applicable, of the templates. We used synthetic and sample
real CT scan images in our experiments. It is found that
template matching is more sensitive to additive noise than
image blurring when tests conducted on synthetic data. On
the sample CT scans small size circular and hollow-circular
templates provided comparable results to human experts.
Index Terms- Shape Representation,
tion, Level Sets, Energy Minimization.
I. INTRODUCTION
With improvements in CT imaging in terms of resolution,
dose, and scanning approach, it has been possible to think
of employing CT scanning in designing and evaluating
fully automatic computer-aided diagnosis (CAD) systems
especially for the thorax (e.g., Boiselle and White, 2002 [1]).
At present, low-dose spiral computed tomography (LDCT)
is of prime interest for screening high risk groups for early
detection of lung cancer (e.g., [2]). Automatic screening
involves detection of nodules then segmentation and classification; i.e., a three-stage process. Our earlier work has
978-1 -4244-1 835-0/07/$25.00 ©2007 IEEE
examined the first two stages (e.g., [11 - 13]) and the
literature on this subject is becoming quite rich reflecting the
importance and urgency of this problem (e.g., [3-12]). Our
CAD system detects the nodules in LDCT images in three
main steps: 1) segmentation of the raw scanning information
to isolate the lung tissues from the rest of the structures in
the chest cavity; 2) extraction of the 3D anatomic structures
(e.g., blood vessels, bronchioles, alveoli, etc., and possible
abnormalities) from the already segmented lung tissues; and
3) identification of the nodules by isolating the true nodules
from other extracted structures. The purpose of the first
two steps is to considerably reduce the searching space.
For the purpose of detection, we used deformable circular,
semicircular and spherical templates (e.g., [11]). We have
also introduced a generalized deformable template in which
nodules are modeled by a 3D deformable prototype, which
closely approximates an empirical marginal probability distribution of image intensities in the real nodules of different
sizes, and is analytically identified from the empirical distribution (e.g., [12]). For the purpose of segmentation, we
introduced an appearance modeling approach for outlining
the lung nodules (e.g., [13][14]). Despite the rich literature in
image analysis of thoracic imaging, there exists no approach
that provides a satisfactory answer/solution to the problem
of automatic nodule detection and segmentation, and the
process of nodule classification (i.e., assigning pathology to
a particular nodule) has a long way to go. Hence, the entire
front-end of CAD systems based on LDCT imaging is wide
open. We will not attempt any survey in this paper of current
approaches; in fact we will go back to the original step, i.e.,
nodule detection
will study the effectiveness of template
a common approach for many of the CAD
matching which isand
studies in the literature.
This paper is organized as follows; section II discusses design issues of parametric templates used in nodule detection
using the a priori knowledge of the shape and gray level
distribution of real nodules. Template matching process is
presented in section III. Section VI presents our experimental
results on synthetic and real data. Conclusions and future
work is presented in section V.
1029
u,,,,,,~~**** -
Fig. 3. Parametric templates of various sizes. First column:
Circular templates, second column: Hollow circular templates and the rest columns show semi-circular templates
f000g - :-- -- - -~~~~~~~~~~~~~~~00
Fig. 1. An ensemble of manually segmented lung nodules
taking various sizes and shapes.
for different orientations.
OA24
R
1o0
50
0
15D
200
250
Fig. 4. The gray level distrib ion for a circular template
where a typical nodule histogram with exponential-like form
is shown (e.g., [3][11][12]).
0
Fig. 2.
15
io¶
20
25
DJi lawnc, Ifbro t~
hrnoduid denter mea
30
35
tue
com
40
in
The radial gray level distribution of the ensemble
of nodules in Fig 1. The bars show the variations from the
mean value.
II. PARAMETRIC TEMPLATE DESIGN
s - u
t
e
nodule models used in our study.
In [13] we used the Gaussian symmetric model (see also
Lee, 2001 [3]) to automatically generate the gray level
distribution of the nodules given the radius R and the
histogram of typical nodule prototypes which were obtained
by expert radiologists. The equations for the template gray
level distribution are given by:
R(ln(qma,,,) -ln(qmjmi)<(1
qoel
usedi study.
q( ed thexpau p))an
r <oR
(2)
valuesq3m)atx and
quoaic determine the range of the
p
Nodules may take various topologies and shapes in a
<
scan. Fig.1 shows an ensemble of nodules that
has been manually segmented. Fig. 2 shows the gray level
The
distribution from the centroid of the nodules calculated at nodules gray levels, and are estimated from the gray level
distances. The main characteristic among various histogram of the CT scans as will be shown later (e.g., Fig
equi-radial
corrlespodn
sphrical,cupogis,
and dhaesks Thsi
hc m ila rity2)
rtonwti
a
si)
measure (eg. cReltio)
nodules
is the woul
factbae
that the
gray level distribution tends to
6).
be concentrated around a region with an exponential decay.
ndulstr Widelyused1parametric.nodul
aretspecified,h fonroaiveshpef
theb aradusundes
calculated
btndlsgaetween,
ansall wsindowdfresmbln
models
inA2DE
reScircuIII
TEMUPLATE.
MATCHING the noduleve
given CT
noue
shtthe
tefcgray leveldistribution.Fg3ilutae so
enisotropi
be concentrated around a region with an exponential decay103
moe6n)h.ein o h mg.I hcreainsoei
Le J.- P hrbiy DEMity Fd,1th
Gf
R0,
__ ,,, C 1~~~~~~~~~~~~~~~~~~~~~~~~~~~~~A5
_
015
0.01
Fig. 5. Left: The generated synthetic image containing ideal
1f100
shapes of nodules with different sizes. Right: the ground
truth generated from the synthetic image using gray level
thresholding based on the image's histogram.
___ay___
III-A. Synthetic Data
A synthetic image was generated with ideal nodule
shapes and different sizes (circular, hollow-circular and
semi-circular
with dits
Is
We generated
semi-circular with different orientations),
ground truth using gray level thresholding over its histogram
(see Fig. 5). The range of gray levels of the synthetic image
was learnt during a training phase using the histogram of
real nodules (see Fig. 6).
We have applied circular average filter with different sizes
ranging from 5 to 20 to obtain blurred versions of the
synthetic image as shown in Fig. 7.
We have also added white Gaussian noise with different
variances ranging from 0.2 to 1 (Fig. 8 shows the first four
noisy images).
Template matching was then applied using templates of
different shapes and sizes. The templates were generated
dynamically for each given image. The histogram of the
given image was used to extract the range of gray levels
assumed by the nodules by fitting two Gaussians using the
EM algorithm. The total number of templates tested for
each image was 1020 templates. Circular template is defined
by its radius ranging from 1 to 40 pixels, hollow-circular
template is defined by the distance between the inner and
outer circular which ranges from 1 to 40 pixels, while semicircular template is defined by its radius (from 1 to 40
pixels) and its orientation (angle of inclination measured
with respect to the x-axis). These values were not empirically
chosen; rather they were learnt during the training phase of
Our study using real nodules images.
2200
2
_
_
_
N
U 025
above a certain level then a nodule candidate is declared. In
this paper we used the sum of the square difference between
the image and the template as the similarity measure. The
location of the nodule may be given by its centroid which
may be taken at the location in the image (or volume) giving
maximum similarity score. This paper provides template
matching results in the 2D case using synthetic data and
CT slices.
I&D
Gayleves
2;0
3m
&
t
i
0
1
60
30
Graylevets
Fig. 6. Top: the probability density function ofthe gray levels
of CT images containing lung liquid and nodules extracted
from the average histogram taken from these images, using
the EM algorithm, the nodules gray levels were
extracted
shown inrthe tto nduter gray wlevels
assumed by the nodules was
leart.
Fig. 7. Circular average filter with different sizes (5, 10,
15,and 20 respectively) was used to blur the synthetic image.
1031
Fig. 8. White Gaussian noise is added with variance 0.2, 0.4,
0.6 and 0.8 respectively.
Fig. 10. Images segmented by level-sets approach [15].
to exract th
threhol use
E
Fig. 9. Sample images of CT scan image. These images
were segmented to isolate the lung tissues. Further, an expert
radiologist outlined the nodules.
III-B. Real Data
Same experiments were conducted using real nodule
images (Fig. 9shows sample images). The images were
segmented using level-sets approach [15], (see Fig. 10).
The EM algorithm was used to fit a mixture of Gaussians
over the image's histogram in order to extract the range of
gray levels assumed by the candidate nodule regions, hence
the lung liquid regions were removed from the search space.
Fig. 11 shows the histogram of the sample images shown in
Fig. 9. Fig. 12 shows the result of removing lung liquid
regions using the EM algorithm,
I
MI
gray,,,_,,,,,,,,,
levl
asum by th
(0
AI w
Fig. It. Images' histograms, where the red line shows the
threshold used to extract the gray levels assume by the
candidate nodule regions.
To check the effectiveness of the template matching results on the sample CT slices, the nodules were outlined
independently by an experienced radiologist (Dr. Aboelghar,
co-author of this article and provider of the CT data for this
project). His labeling is shown in Fig. 13.
IV. EXPERIMENTAL RESULTS
tV-A. Synthetic data results
Experimental results were conducted to investigate the
sensitivity of nodule detection using template matching to
shape, size and orientation. As shown in Fig. 14, in the
ideal case where there is neither bluffing nor noise, the
hit rate is decreased when using larger radii for circular
1032
Hiitet h
LII~~~
~
ithtbitmg
~ ~ ~~~~~~~~~~~~~~~~~~
t Hit
MO
iuofghd
he
it r" itd ird
t
lad
utHidb*~alrt
4iAte
Hit7l atedothebulrred
Fig. 12. Lung liquid regions were removed using EM
algorithm to reduce the search space of nodule detection.
mages
Hit rteoIh, Ythetjdil-g
e b~e t&e
Hi aeo
it
it
it
2t iHt
Fig. 14. Top: hit rate resulted from using circular templates.
Bottom: hit rate resulted from using hollow-circular tem-
plates.
template size. Yet noisy images record the lowest hit rate
compared to the ideal and blurred case (Fig. 16).
IV-B. Real data results
Fig. 17 and 18 shows the hit rates for different template
shapes. It can be inferred for larger circular and hollowcircular templates, the hit rate decays significantly. However
this is not the case for semi-circular templates, yet the
Fig. 13. Ground truth labeling of the nodules by a human
expert. The regions reported as nodules were circled in red.
theidalas,heitraticraseuin lage raiu
for te sei-cirulartemplte, owevethibehaiorFsino
and hollow
decrease is
templates; however
mainaindcircular
fral oienatins.In
cse f tethisburrdiage
more
in
noticeable
case
of blufred images. In case of noisy
the ero rietatin mintans hehghethiraewhle
images, the hit rate for circular and semi-circular templates is
orenttios hve
smilr
ith case,
espct
o te
low compared
to the
idealbhavor
and blurred
however,
very othe
the hit rate is increasing (with small slope) along circular
whiletuth
small slope)
radius,
3. Grund
lbelinof (with
he noules
y a hman
templateFig.
decreasing
along
the hollow-circular template size. As shown in Fig. 15, in
zeoorientatinsreordevuteryor lowheorate.
v
ttos
hl
te
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tissure.
thi paper presentanAND
fiurstdcattemptiat
quntfiatono
V. CONCLUSION
FUTURE RESEARCH
This work aimed at examination of the accuracy of the
the tepate machn processh fotraenodul difetecin.
Onmlyt
detection of the
nodules
template
matching
approach for
spaamesItrica
tempaewnerred
considergedcincla
whiclungthelgray
as shown in low dose chest CT scans. Accuracy can be
tep ats, whelast size werey determicnedyHoempircarleaisribto
measured in terms of the location of the nodules and their
ziall
grayescale
ditheribuin ttion
fa ofnsembhle
It can also
be measured
terms
ofhe
shapes.usiengtheio
separability
from the anatomical structures. This is a very complicated
task
since nodules mayvey
takelo1033e
various shapes, spatial support,
oienttionsrecored
and may indeed resemble anatomical structures in the lungs
SemWi Ciruuar Tnpa
9esupt
s
ltzs Wth Diffien Radiitad Oridenttioans indegrees
CtouirTempties
R F
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Fig. 15. Hit rate resulted from using semi-circular templates
with different orientations, results from the synthetic images
(top), results from the blurred images(bottom).
sn
So0
n
i
lrrde7r
_
20
5o
eTemplatem tVidth
s o0
(rb r2n
25
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Fig. 17. Hit rates reported using real images using circular
templates (top) and hollow-circular templates (bottom).
hlcwrcu-crclarmtes behaveds, milarly regardless the template
Fig. 16. Hit rate resulted fromusigsemi-circular t lat e
size when using the synthetic data, however there is a size
l g 0 N >>
k
~~~~beyond which horizontal ternplate orientation (0 and 360
withXdifferent orientations for nois images.
yetthedegrees) outperform other orientations, while this behavW \4 1t
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foun thatt
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<~~~~~~~~~~~~~~borientation
aS human, significantly
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for0
other
orientations.
Yet
real nodulethathavebeenmanuallysegmented.Ground
wtemplate matching was found to be sensitive to additive noise
truth was generated using syntheticimagesoftempla
recording a very low hit rate when compare to the blurred
ofvaiousshap,siean oriaio. Te gcase. The template matching was also performed on four
generaedusnganexponntial modelithpaamslices of real CT data in which the nodules were identified
by a human expert. It was found that for larger circular and
hollow-circular templates, the hit rate decays significantly.
Fig. 16. Hit rate resulted from using semi-circular templates
However this is not the case for semi-circular templates,
with different orientations for noisy images.
yet the zero orientation outperform other orientations, while
other orientations recorded very low rate. Current efforts are
directed at more experimentation on real data collected with
real nodules that have been manually segmented. Ground a well specified image acquisition approach. The ground
truth was generated using synthetic images, temhtsoes
truth will be specified by more than one radiologist and the
overall robustness of template matching will be quantified.
of various shape, size and orientation. The gray scale was
generated using an exponential model with parameters es- We have intentionally ignored the standard subsequent steps
of reducing false positives in order to test the effectiveness of
timated from the nodule ensemble. The synthetic image
the template matching process itself. Of course, reduction of
was altered by both blurring and Gaussian noise of varying
I
lo
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2.
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3
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Oetoh
Semi
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[7] H. Arimura, S. Katsuragawa, K. Suzuki, F. Li, J.
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Fig. 18. Hit rates reported using real images for semi-circular
templates with different orientations.
Trans. on Medical Imaging, vol. 24, no. 3, pp.409-423,
March 2005.
[10] I. Sluimer, A. Schilham, M. Prokop, and B. van GinVI. ACKNOWLEDGEMENTS
neken,"Computer Analysis of Computed Tomography
Scans of the Lung: A Survey," IEEE Transactions on
Medical Imaging, vol. 25, NO. 4, pp. 385-405, April,
This work has been supported by the Kentucky Lung
Cancer Program.
2006.
X 5
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to
inplraVpteRadilus25
3°
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