J. Biomedical Science and Engineering, 2012, 5, 409-415 JBiSE
http://dx.doi.org/10.4236/jbise.2012.58052 Published Online August 2012 (http://www.SciRP.org/journal/jbise/)
Reduction of artifacts in dental cone beam CT images to
improve the three dimensional image reconstruction
Issa Ibraheem
Department of Biomedical Engineering, Damascus University, Damascus, Syria
Email: issa.ibraheem@gmail.com
Received 12 August 2011; revised 10 January 2012; accepted 14 June 2012
Cone-beam CT (CBCT) scanners are based on volu-
metric tomography, using a 2D extended digital array
providing an area detector [1,2]. Compared to tradi-
tional CT, CBCT has many advantages, such as less
X-ray beam limitation, and rapid scan time, etc.
However, in CBCT images the x-ray beam has lower
mean kilovolt (peak) energy, so the metal artifact is
more pronounced on. The position of the shadowed
region in other views can be tracked by projecting the
3D coordinates of the object. Automatic image seg-
mentation was used to replace the pixels inside the
metal object with the boundary pixels. The modified
projection data, using synthetically Radon Transfor-
mation, were then used to reconstruct a new back
projected CBCT image. In this paper, we present a
method, based on the morphological, area and pixel
operators, which we applied on the Radon trans-
formed image, to reduce the metal artifacts in CBCT,
then we built the Radon back-project images using
the radon invers transformation. The artifacts effects
on the 3d-reconstruction is that, the soft tissues ap-
pears as bones or teeth. For the preprocessing of the
CBCT images, two methods are used to recognize the
noisy black areas that the first depends on threshold-
ing and closing algorithm, and the second depends on
tracing boundaries after using thresholding algorithm
too. The intensity of these areas is the lowest in the
image than other tissues, so we profit this property to
detect the edges of these areas. These two methods
are applied on phantom and patient image data. It
deals with reconstructed CBCT dicom images and
can effectively reduce such metal artifacts. Due to the
data of the constructed images are corrupted by these
metal artifacts, qualitative and quantitative analysis
of CBCT images is very essential.
Keywords: CBCT; Artifact; Medical Image Processing;
CT; Image Reconstruction
Cone beam X-ray CT (CBCT) is a relatively recent in-
stallment in the growing inventory of clinical CT tech-
nologies [1-3]. Although the first prototype clinical CBCT
scanner was adapted for angiographic applications in
1982, the emergence of commercial CBCT scanners was
delayed for more than a decade. The arrival of marketable
scanners in the last 10 years has been, in part, facilitated
by parallel advancements in flat panel detector (FPD)
technology, improved computing power, and the rela-
tively low power requirements of the X-ray tubes used in
CBCT. These advancements have allowed CBCT scan-
ners to be sufficiently inexpensive and compact for oper-
ation in office-based head and neck as well as dental
imaging applications [2,3].
Obvious advantages of such a system, which provides
a shorter examination time, include the reduction of im-
age sharpness caused by the translation of the patient,
reduced image distortion due to internal patient move-
ments, and increased X-ray tube efficiency. However, its
main disadvantage, especially with larger FOVs, is a
limitation in image quality related to noise and contrast
resolution because of the detection of large amounts of
scattered radiation [1,3].
CBCT metal artifact reduction has a problem that the
metallic objects in a human body have much higher at-
tenuation coefficients than that of soft-tissue and produce
annoying artifacts such as streak and shade artifacts.
These artifacts significantly degrade the visual quality of
the image and distort the skeletal structure close to me-
tallic objects. The two main reasons to produce metal
artifacts are photon starvation and beam hardening. The
number of photons which pass through the metallic ob-
jects is much less than the number of photons passing
through the non-metallic objects. Due to this photon
starvation, the signal-to-noise ratio (SNR) becomes low
in the measured projection data. The noise produces
streak artifacts in a reconstructed CT image [4-7]. Mean-
while, the beam hardening effect makes the logarithm of
the measured X-ray photons nonlinear to the pass length
I. Ibraheem / J. Biomedical Science and Engineering 5 (2012) 409-415
and the corresponding attenuation coefficients of an ob-
ject. This beam hardening effect becomes more serious
when the X-ray passes the material having a high at-
tenuation coefficient and when its path length increases.
In the case that the X-ray passes through two or more
metallic objects, the above two conditions are applicable
and the strong shade artifacts appear in a reconstructed
CBCT image [7,8]. Little studies have been performed to
reduce metal artifacts on dicom CBCT images that many
studies have been performed on the raw projections of
CBCT. We applied our study on dicom images, because
we haven’t the technical abilities to acquire raw projec-
tions on our laboratories. We’ll study the problem on
dicom images which are produced by “Picasso PRO”
CBCT which is made by VATECH Co., that its FOV
(Field of view) is 12 cm × 7 cm, Kv is 85 and mA is 4.
We read the dicom image using Matlab software and a
simple of primary image was illustrated in (Figure 1A),
that the contrast of it was very low, so after applying
contrast processing, the image became more clear (Fig-
ure 1B).
2.1. Double Thresholding and Closing Algorithm
Using Double thresholding to the contrast-adjusted im-
age between 0, 0.1, we acquired the following image
witch’s illustrated in (Figure 2A), and then we applied
closing algorithm which performs closing with a struc-
turing element that specifies its neighborhood as follow-
ing (Figure 2B). We could determine noisy black areas
and other anatomic organs (spine), that there isn’t any
problem by detecting those other organs because they are
outside our interesting.
2.2. Otsu’s Thresholding and Boundaries
We applied thresholding by Otsu’s method through
measuring the effectiveness of a threshold computation.
For this metric, the lower bound of 0 represents a monotone
Figure 1. A. Sample of an original dicom CBCT-image; B. After
contrast enhancement.
image, and the UPPER bound of 1 represents a two va-
lued image [6,7], then we applied Trace Boundaries al-
gorithm which traces boundaries in binary images, where
nonzero pixels represent objects and 0 pixels represent
the background [4,8-10]. The result is illustrated in (Fig-
ure 3) that we could determine noisy black areas and
other unimportant anatomic organs, so by subtracting
these areas, we can acquire an image without noisy black
areas and keep other anatomic organs undamaged. The
previous two methods are step1 towards enhancement
CBCT images by reduction of metal artifact.
2.3. Radon Transformation (RT)
We developed the following algorithm to reduce the
metal artifact in CBCT images. The image was processed
using radon transformation which computes projections
of an image matrix along specified directions, and com-
putes the line integrals from multiple sources along pa-
rallel paths, or beams, in a certain direction [5,11-13].
The intensity of each pixel in the image will be dis-
played as carve lines with a value depends on the inten-
sity level of this pixel, these carves are called “Sino-
gram”, because the radon transformation of source point
is a sinusoid, show (Figures 4(b) and 5A).
Fortunately; the (RT) has a well-defined inverse. In
order to invert the transform, we need projection data
spanning 180 degrees. The inverse transformation is used
Figure 2. A. After applying double thresholding to the Figure
2A between 0, 0.1; B. After applying closing algorithm.
Figure 3. After applying Otsu’s thresh-
olding and Boundaries tracing.
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I. Ibraheem / J. Biomedical Science and Engineering 5 (2012) 409-415 411
to reconstruct images from raw projections. In general,
increasing the number of projections (reducing angular
step), improves image quality.
The (RT) of a distribution function (image data) f(x, y)
is given by:
 
,,δcossind dRTfx yxyx y
 
 (1)
where δ is the Dirac delta function, φ is the angle and ξ is
the smallest distance to the origin of the coordinate sys-
tem. The Radon transform for a set of parameters (ξ, φ)
is the line integral through the image f(x, y), where the
line is positioned corresponding to the value of (ξ, φ) as
it illustrated in Figure 6. The sinogram RT(ξ, φ) has
many important mathematical properties as:
 
,,πRT RT
 
We apply (RT) on the CBCT image towards counter
clockwise from the horizontal position to the line on which
the detector array is located, as shown in Figure 5B.
Then we apply the thresholding on the radon trans-
formed image. We’ll notice that the noisy data will be
Figure 4. (a) Original dicom CBCT-image; (b)
Radontransformation of image (a).
removed of each point in the original image in all projec-
tions angels from 0 to 360 degree. The resulting (RT)
after applying thresholding is shown in Figure 5A, and
the reconstructed image of the processed (RT) is shown
in Figure 5B.
This task can be reduced by selecting of the threshold
value T which optimizes a predefined criterion [12,13].
Once T is computed, the thresholded image:
f(x, y), 1 x M, 1 y N that can be generated by
assigning the following values:
  
0if ,
xy T
Ixy Ixy
Each beam is detected on the side of the body opposite
from the beam source, and its detected intensity is com-
pared to its intensity at the source. Most medical imaging
Figure 5. (a) After applying thresholding on the
Radon transformation of the image; (b) Reconstructed
image of the processed Radon transformation.
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I. Ibraheem / J. Biomedical Science and Engineering 5 (2012) 409-415
systems separately reconstruct two-dimensional slices of
a three-dimensional object. If necessary, these recon-
structed two-dimensional slices may be combined to cre-
ate a three-dimensional representation of the object being
To reconstruct an Image we need to define an array of
projection angles (i.e. φ = 0 to 180 with step of 1 degree)
then we calculate the transformation of each value of φ
depending on the corresponding coordinates x as it shown
in Figure 4(b). Then we return the reconstructed image
from projections which taken at angles defined by φ us-
ing the reverse radon transformation [14-17], which ma-
thematically is defined as:
inv xy RTxy
Geometrically, the backprojection operation simply
prop- agates the measured sinogram back into the image
space along the projection paths, show Figure 7.
By using the central slice theorem (CST), which re- lates
with F(νx, νy); the 2D Fourier transform (FT) of f(x, y), and
RT(υ, φ); the 1D FT of RT(ξ, φ), show Figure 8. Mathe-
matically, the CST is given by:
,cos,siRT F
 
The CST theorem states that the value of the 2D FT of
f(x, y) along a line at the inclination angle φ is given by
Figure 6. Coordinate system for the Radon Transformation.
Figure 7. Geometrical interpretation of back-
the 1D FT of RT(ξ, φ); the projection profile of the sino-
gram acquired at angle φ. Hence, with enough projec-
tions, RT(υ, φ) can fill the νx, νy space to generate F(νx,
νy). In the Fourier space, Equation (2) becomes:
,π,RT RT
To synthesize a parallel projection of angle finds all
rays such that:
 
 (7)
For this projections are needed of the angular range:
max max
 
 
with βmax as maximum fan angle shown in Figure 9(a).
Focu s
arcsin R
An object point r can be reconstructed exactly if it
sees a scan path segment of angular range pi. Thus, an
image part can be reconstructed without acquiring com-
plete data of the object (super short scan). Specific algo-
rithms are needed for reconstruction from a super short-
There are several approaches to the 3D surface genera-
Figure 8. Central slice theorem.
(a) (b)
Figure 9. (a) Fan beam Principe; (b) Fan beam field of view
(black lines) and parallel beam field of view (red lines).
Copyright © 2012 SciRes. OPEN ACCESS
I. Ibraheem / J. Biomedical Science and Engineering 5 (2012) 409-415 413
tion problem. An early technique [17,18] starts with
contours of the surface to be constructed and connects
contours on consecutive slices with triangles. Unfortu-
nately, if more than one contour of surface exists on a
slice, ambiguities arise when determining which contours
to connect [19-21]. Interactive intervention by the user
can overcome some of these ambiguities [8,10,20,21];
however, in a clinical environment, user interaction should
be kept to a minimum.
We used an approach to locate the surface in a logical
cube created from eight pixels; four each from two adja-
cent slices. There are two primary steps in our approach
to the surface construction problem, refer to Figure 10.
First, to locate the surface in the data cube created
from eight pixels, the first four from slice k while the
second four from k + 1 slice as it shown in Figure 10.
We create triangles with locate the surface correspond-
ing to a specified chosen value and. Then, we calculate
the norm to the surface at each point of the Triangle, that
to ensure a quality image a zero and lies outside the surface.
The surface intersects those cube edges where one
vertex lies outside the surface, which gets the value (1)
and the other one lies inside the surface, which get the
value (0). With this rule, we determine the topology of
the surface within a cube, finding the location of the in-
tersection. With this assumption, we determine the to-
pology of the surface within a cube, finding the location
of the intersection later [13,14,21,22]. Since there are
eight vertices in each cube and two slates, inside and
outside, there are only 28 = 256 ways a surface can inter-
sect the cube.
By enumerating these 256 cases, we create a table to
look up surface edge intersections, given the labeling of
cubes vertices, refer to Figure10. The table contains the
edges intersected for each case. The final step in march-
ing cubes, refer to Figure11 calculates a unit normal for
each triangle vertex. The rendering algorithms use this
normal to produce the image in Figures 12 and 13.
Figure 10. Marching cubes to locate the surface using eight
pixels, four each from two different slices.
Figure 11. Marching Cube image of CBCT slices.
Figure 12. 3D reconstruction isoline of CBCT slices, soft
tissues is reconstructed as bone structures.
Figure 13. 3D reconstruction using isosurface applying linear
vertical smoothing.
Figure 14 shows the topological marching cube image
of CBCT-dicom data.
In Figure 12, a few parts of the soft tissues is recon-
structed as a bone structures that because the reduction of
the shadow and brightness in the slides quantitative, de-
pends on the chosen threshold, was not suitable. Using
an adaptive threshold value for each slide and applying a
linear vertical smoothing in z-Direction (distance be-
tween the slides in z-Direction) in the Dicom cube lead
to more suitable reduction of the artifacts as it shown in
Figure 13. The threshold was calculated using the the
next Equation:
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I. Ibraheem / J. Biomedical Science and Engineering 5 (2012) 409-415
  
max min
for1, 2,...
Ixy Ixy
where, I(x, y)k-min, I(x, y)k-max the minimal and maximal
intinsities in the kth slide.
Value of each voxel is the value of the correlative
pixel, which is often the gray level of pixel. After ar-
ranging of parallel slices, rendering techniques will be
selectively use to perform the volume data. A surface of
constant density has a zero gradient component along the
surface tangential direction; consequently, the direction
of the gradient vector g is normal to the surface. We can
use this fact to determine surface normal vector n if the
magnitude of the gradient g is nonzero. Fortunately,
at the surface of interest between two tissue types of dif-
ferent densities, the gradient vector is nonzero. The gra-
dient vector g is the derivative of the density function.
We can recognize noisy black areas in CBCT images
when metal objects exist in the mouth depending on
thresholding and closing algorithm, or by depending on
tracing boundaries after using thresholding algorithm.
That means that we can process these areas in the future
by replacement it with right data by profiting of neigh-
bours in the same image and neighbours in the previous
and next dicom images.
These methods are step1 towards enhancement CBCT
images by reduction of metal artifact. The Marching
cubes as an algorithm for 3D surface construction, com-
plements CBCT data by giving 3D views of the anatomy.
The algorithm uses a case table of edge intersections to
describe how a surface cuts through each cube in a 3D
data set.
Additional realism is achieved by the calculation, from
the original data, of the normalized gradient. The result-
ing polygonal structure can be displayed on conventional
Figure 14. Cub order.
graphics display systems. Although these models often
contain large numbers of triangles, surface cutting and
connectivity can reduce this number.
Recently we developed the surface construction algo-
rithm that generates points rather than triangles and can
effectively reduce such metal and dark areas artifacts in
the reconstructed 3D-images based on radon- and radon
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