2012 International Conference on Biomedical Engineering and Biotechnology
Automatic Rib Segmentation in Chest CT Volume Data
Li Zhang
Xiaodong Li
Qingmao Hu
Shenzhen Institutes of Advanced
Technology, Chinese Academy of
Sciences, Shenzhen, China
The Chinese University of Hong
Kong, Hong Kong, China
Shenzhen Key Laboratory of
Neuro-Psychiatric Modulation,
Shenzhen, China
Linyi People’s Hospital,
Shandong Province, China
Shenzhen Institutes of Advanced
Technology, Chinese Academy of
Sciences, Shenzhen, China
The Chinese University of Hong
Kong, Hong Kong, China
Shenzhen Key Laboratory of
Neuro-Psychiatric Modulation,
Shenzhen, China
[email protected]
with the sternum. The ribs have stable shape, and map to
high intensities in CT data. The rib structures enclose the
complete chest and part abdomen. Furthermore they are
symmetrical and highly ordered. These rib features can be
used for reliable registration while the segmented ribs as
reference objects can help segment other structures [3-4].
Abstract—An automatic segmentation method for extraction of
human rib structures from chest CT volume data is presented.
Segmentation is initiated from the middle coronal slice to
attain complete and isolated 12 pairs of ribs with a recursive
tracking on coronal slices spreading from the middle coronal
slice. At each coronal slice, the lung contours are extracted;
candidate rib regions are derived from thresholding, and
refined by adding constraints on shape and location with
respect to the lung contour and centroids of rib regions of the
former coronal slice. Anatomical and radiological prior
knowledge has been explored to ignore those rib regions
connected to spine for breaking the connection with spine.
Appropriate thresholds are chosen so that lung regions can be
binarized, the cartilage connecting ribs and sternum are
binarized as background to break the connection between the
sternum and ribs. Our method is tested on 15 CT data sets.
Experiments show that radiologists are satisfied with the
extracted rib regions on coronal slices, and only those rib
regions connected to spine are discarded (5.5% of all 2D rib
regions). The method provides a foundation for further
investigation on computer-aided diagnosis of rib fractures.
To the best of our knowledge, rib segmentation has not
been paid much attention. In [5-7], the ribs, sternums and
spines which connected as one region was segmented.
Several methods which used to segment elongated structures
were applied to segment the ribs in CT data [8-9]. In [5] a
tracking method based on region growing was described,
which needs manual seeds to proceed on slices one by one.
In [10] the method was based on locally adaptive thresholds
and 3D region growing.
In [11-12], a centerline tracing method previously
developed for the vasculature segmentation was proposed
which is initiated with the rib seed points found from the
middle coronal slice. In [13-14], a supervised method was
proposed to obtain the rib structures with a region growing
process, in which the seeds were primitives of rib centerlines
labeled from non-rib primitives using a trained classifier. [13]
might miss the 11th and 12th pairs of ribs, while [14] could
need a long computational time. In [15], a rib cage model
constructed from training set was used. In [16], each rib was
grown from the seed region which is detected based on the
identified spinal centerline.
Keywords- Rib Segmentation; Computed Tomography;
Threshold; Tracking
I.
INTRODUCTION
The diagnosis for various kinds of diseases has been
becoming efficient due to the progress of medical imaging
technology. But meanwhile large amount of data is produced
by the three dimensional (3D) imaging modalities, such as
CT imaging, which is difficult to be interpreted by
radiologists. Hence, post-processing methods by the
computer are being developed to solve this problem. [1]
gives numerous examples, of which automated rib
segmentation is the one we are interested in.
Using region growing based on grayscale is a feasible
way. However, it’s difficult to separate ribs from spines and
sternum. In this paper, we propose an algorithm in which a
recursive tracking process is performed while prior
knowledge is employed to get rid of spine and sternum.
The rest of this paper is organized as follows. Section II
describes each stage of the algorithm. Section III presents
experiment results. Section IV is devoted to discussion and
conclusions.
Rib segmentation is of great significance to rib cage
visualizations and to computer-aided rib abnormalities
diagnosis [2]. Human body has 12 pairs of ribs which are
connected with the vertebral column at the posterior end,
while at the anterior end the upper 10 pairs are connected
978-0-7695-4706-0/12 $26.00 © 2012 IEEE
DOI 10.1109/iCBEB.2012.89
750
II.
METHOD
Chest CT Scans
Our rib extraction algorithm includes two main steps.
First, rib regions are extracted in a coronal slice close to the
center of the chest (middle coronal slice). Second, a tracking
process is performed from the middle coronal slice in
posterior and anterior directions. At each coronal slice, the
segmentation consists of finding lung contour, followed by
delineating rib regions incorporating radiological and
anatomical knowledge. The pipeline of our method is
illustrated in Fig. 1, and the detailed steps are described
below. The 3D coordinate system follows the following
convention: the x axis is from left to right, y from anterior to
posterior and z from superior to inferior.
Preprocessing
Extraction of lung contour and ROI
Segmentation of middle coronal slice
Tracking segmentation
Rib cage
A. Prepocessing
Various imaging protocols adopted by technicians in
different medical centers causes difficulties for automatic
image analysis. For example, different physicians may
employ different slice thickness in the axial space during
chest CT scanning. Noise in data from high and low dose
scans may have different levels. The high noise in low dose
CT data can lead to obscure rib boundaries.
Figure 1. The pipelines of our proposec method.
set as within 20 pixels).
Because of noises and partial volume effect, an elliptic
rib region may appear in more than one region. Hence, a
close operation with a square SE of 2 pixels in side is
performed to merge small regions which originally belong to
one rib region.
Hence, in the image preprocessing step, we have to deal
with these two major issues. The original data is converted
into isotropic data and a median filter is selected for noise
removal.
Applying all the rules above, we extract all rib regions.
And the center points of these regions are saved and denoted
as an array p256 (i), with p256 (i) = (xi256, 256, zi256) as the
starting points of the following tracking procedure. Due to
the extent of the CT scan, 9-11 pairs of rib regions and
corresponding centroids are obtained, by which the tracking
spreads in both directions.
B. Extraction of Lung Contour
The CT intensity value of lung ranges from -500 to -900
HU (Hounsfield Unit). We select a threshold of -500 HU for
binarization. A morphological close operation with a square
structuring element (SE) of 6 pixels in side is applied to the
binarized image to fill gaps within the lung. At last the lung
contour which helps locate the rib regions on each coronal
slice is obtained by a gradient magnitude filter.
D. Tracking
Starting at each centroid point from the above step,
tracking process is initiated in both directions, from the
center of the chest to the posterior vertebral column (from
the 256th to 512th), and to the anterior sternum (from the 256th
to 1st).
C. Processing of Middle Coronal Slice
In order to start the following tracking segmentation, we first
choose the middle coronal slice to process. The data size is in
the range of 512×512×400 ~512×512×700 voxels, so we
usually choose the 256th coronal slice as the middle coronal
slice. This slice intersects with almost all of the ribs, but does
not contain any spine or sternum region. And the ribs on this
coronal slice are small regions which are similar to ellipses
in shape, locating uniformly around the leftmost and
rightmost boundaries of the lung (Fig. 2 (b)).
For the 255th coronal slice, the same threshold for bone is
employed to binarize the 255th coronal slice. Then, rib
regions are searched around the corresponding points (xi256,
255, zi256) within a circular area of a radius of 50 pixels
radius in the binarized image. Spreading in this direction, rib
regions are constrained to be within 20 pixels of the lung
boundary in horizontal direction. After all the rib regions are
found, the centroids of ribs are updated as p255 (i) = (xi255,
255, zi255) for processing the 254th coronal slice. This process
goes on until the 1st coronal slice is processed. As the
cartilage which connects the sternum and ribs have a density
below 110 HU, the thresholding process to find ribs will not
contain any sternum.
The threshold at 110 HU for bone is employed to
binarize the coronal slice, and foreground regions are labeled
as light, which are the ROIs (region of interest). Size
constrains are employed to eliminate most of the non-rib
regions. Ribs have a rather stable size on each coronal slice,
which is used to exclude non-rib foregrounds with too large
or too small sizes. Operationally, the upper and lower sizes
are set as 400 and 20 pixels, respectively. This constraint is
employed to all coronal slices. Rib regions are required to be
close to the lung contour (horizontal distance is operationally
For the 257th coronal slice, there are a few differences.
On the one hand, we obtain rib regions through the similar
steps to those of to the 255th coronal slice, only the points to
search for ribs are changed to (xi256, 257, zi256). On the other
hand, we search rib regions around the contour of the middle
751
boundaries and vertically 100 pixels below the most inferior
pair of ribs. This extra step functions in 2 scenarios. The first
is to prevent missing of rib regions which are between the
left and right lung contour when images contain spine
regions (marked by yellow ellipses in Fig. 2(d) ), while the
second is to find new rib regions ( marked by yellow ellipses
in Fig. 3), i.e., the 10th , 11th, and 12th pairs of ribs. As shown
in Fig. 3, if the new region located vertically below the most
inferior pair of ribs appears in not less than three consecutive
slices irrespective of their small sizes, it will be considered as
a rib region in the consecutive coronal slices. After all the rib
regions are found, the centroids of ribs are updated as p257 (i)
= (xi257, 257, zi257) for processing the 258th coronal slice. This
process goes on until the 512th coronal slice is processed. As
large size foreground regions are discarded which
corresponds to foreground regions with spine, the connection
with spine is broken. The additional searching around the
vertical direction of the most inferior pairs of ribs ensures
that 12 pairs of ribs could be segmented and tracked.
III.
Figure 2. (a) The 256th coronal slice; (b) the binarized image; (c)the lung
contour and the rib regions in the 256th image; (d) the 335th binarized slice
in which spine region and rib region between left and right lung boundaries
appear.
EXPERIMENT AND RESULT
In this section, results of each processing step in the
experiments are presented. The original CT dataset were
provided by Shandong Linyi People's Hospital. Among these
dataset, half of them contain rib fractures, and the rest are
normal scans. In our experiments, 15 chest CT dataset were
tested. The original dataset were converted into isotropic data,
of which the resolution in each direction ranges from 0.5 mm
to 0.8 mm.
Figure 3. Result of the 260th , 261th , 262th, 263th coronal slice (from left
to right).
Shown in Fig. 2(c) are the lung contour and the
segmentation result of the 256th slice are. It can be seen that
soft tissue and miscellaneous bones in the original coronal
slice (a) are removed completely and only 21 rib regions (c)
are kept.
Figure 4. Non-rib region and missing rib regions: (a) the 169th coronal
slice; (b) result of the 169th; (c) the 340th ; (d) the 340th binarized slice in
which part rib regions are connecting with spine regions.
Fig. 3 and 4 (b)-(c) show the results of tracking in several
representative slices. The rib regions in different coronal
slices vary in shape and distribution. In Fig. 4 (b), there is a
misjudged rib region which belongs to the clavicle region for
its similar shape feature to that of rib regions (marked by the
yellow ellipse). As shown in Fig. 4 (c), several rib regions
which should originally locate in the yellow ellipse are
missing in the 340th coronal slice while in other three coronal
slices rib regions are complete. For the 340th coronal slice,
the missing rib regions are found by the tracking and
removed later by the size constraint for its connecting with
the spine regions (marked by yellow ellipse in Fig. 4 (d)).
Shown in Fig. 5 is an example of the rib cage constructed
from segmentation of 512 images. We can see that ribs are
almost complete except a little missing in areas connecting
with vertebral column compared with results of manual
segmentation.
Figure 5. The 3D display of the rib cage.
radiologists matched well in most coronal slices. Of the
regions manually identified, 94.5% is extracted by our
method; and of the regions extracted, less than 1% is non-rib
region (marked by a yellow ellipse in Fig. 4 (b)).
Quantitative comparative analysis of the rib regions is
performed in the following way. Radiologists are asked to
count the number of rib regions extracted at coronal slices
and visually compare the quality of extracted rib regions.
Regions obtained by our method and those identified by the
752
IV.
DISCUSSING AND CONCLUSION
[2]
In this paper, we have proposed a rib segmentation
method for chest CT volume data. A prior knowledge based
tracking segmentation is applied for the complete rib
structure. The tracking mechanism and size differences
between ribs and spines play a crucial role in separating ribs
from spines. Thresholds and parameters contained in the
procedure were experimentally determined with several adult
chest CT dataset. Segmentation of the complete set of rib
structures from a data set of 685 image slice on a 2.4 GHz
Dual Processor and 3 GB memory Pentium PC takes about
80 seconds. The rib structure could be used not only to help
for locating the internal organs in the thoracic and abdominal
area by developing relative coordinate systems, but also to
help for the further computer-aided diagnosis of rib fractures.
[3]
[4]
[5]
[6]
By experiments the performance of the segmentation
method proposed in this paper is proved to be satisfying for
short computation time and almost complete rib
visualization. From the quantitative evaluation, we can see
that there are 5.5% of rib regions which are missing for the
disconnection of ribs from the spine, and parts of clavicle
regions are misjudged as rib regions, this part could be
eliminated by considering the extension in y direction (as
most parts of the clavicle regions are more than 20 pixels
away from the lung contour in horizontal direction such that
they were not considered foreground regions). The algorithm
provides a way to derive 12 pairs of isolated ribs by breaking
the connection between ribs and spines without missing
much of the ribs (around 5.5% rib regions closest to spines
are missing). Our future research will focus on visualization
of ribs which could display the information of rib fracture
more directly and clearly. This work is also a part of our
ongoing CAD system for the detection of rib fracture.
[7]
[8]
[9]
[10]
[11]
[12]
ACKNOWLEDGMENT
The authors would greatly thank Jingui Du and Zhenchao
Sun (Shandong Linyi People's Hospital) for providing the
clinical chest CT datasets. This work was supported by
Shenzhen Key Laboratory of Neuro-Psychiatric Modulation.
[13]
[14]
REFERENCES
[1]
[15]
R. M. Summers, “Road maps for advancement of radiologic
computer-aided detection in the 21st century,” Radiology, vol. 229,
no. 1, pp. 11~13, 2003.
[16]
753
A. P. Kiraly, S. Qing, and H. Shen, “A novel visualization method for
the ribs within chest volume data,” SPIE Proceedings: Medical
Imaging, vol. 6141, pp. 51~58, 2006.
H. Shen, and M. Shao, “A thoracic cage coordinate system for
recording pathologies in lung CT volume data,” Proceedings of
Nuclear Medicine Symposium and Medical Imaging Conference,
Portland, USA, 2003.
H. Wang, J. Bai, and Y. Zhang, “A relative thoracic cage coordinate
system for localizing the thoracic organs in chest CT volume data,”
Proceedings of the 27th Annual International Conference of the IEEE
Engineering in Medicine and Biology Society, Shanghai, China, pp.
3257~3260, 2005.
D. Kim, H. Kim, and H. S. Kang, “An object-tracking segmentation
method: vertebra and rib segmentation in CT images,” SPIE
Proceedings: Medical Imaging, vol. 4684, pp. 1662~1671, 2002.
R. M. Rangayyan, S. Banik, and G. S. Boag, “Automatic
segmentation of the ribs and the vertebral column in computed
tomographic images of pediatric patients,” CARS 22nd International
Congress and Exhibition: Computer Assisted Radiology and Surgery,
vol. 3, no. 1, Barcelona, Spain, pp. S42~S44, 2008.
R. M. Rangayyan, H. J. Deglint, and G. S. Boag, “Method for the
automatic detection and segmentation of the spinal canal in computed
tomographic images,” Journal of Electronic Imaging, vol. 15, no. 3,
pp.033007-1~033007-9, 2006.
S. R. Aylward, E. Bullitt, S. Pizer, and D. Eberly, “Intensity ridge and
widths tubular object segmentation and description,” Proceedings of
the Workshop on Mathematical Methods in Biomedical Image
Analysis, pp. 131~138, 1996.
S. R. Aylward and E. Bullit, “Initialization, noise, singularities, and
scale in height ridge traversal for tubular object centerline extraction,”
IEEE Transactions on Medical Imaging, vol. 21, no. 2, pp. 61~75,
2002.
Y. Kang, K. Engelke, and W. A. Kalender, “A new accurate and
precise 3-D segmentation method for skeletal structures in volumetric
CT data,” IEEE Transactions on Medical Imaging , vol. 22, no. 5, pp.
586~598, 2003.
H. Shen, L. Liang, M. Shao, and S. Qing, “Tracing based
segmentation for the labeling of individual rib structures in chest CT
volume data,” MICCAI, vol. 3217, pp. 967~974, 2004.
H. Shen, L. Liang, M. Shao, S. Qing, and D. P. Naidich, “A system
for automatic rib labeling in chest CT volume data,” RSNA, Chicago,
USA, 2004.
J. Staal, B. V. Ginneken, and M. A. Viergever, “Automatic rib
segmentation in CT data”, Computer Science, vol. 3117, pp. 193 ~
204, 2004.
J. Staal, B. V. Ginneken, and M. A. Viergever, “Automatic rib
segmentation and labeling in computed tomography scans using a
general framework for detection, recognition and segmentation of
objects in volumetric data”, Medical Image Analysis, vol. 11, pp.
35~46, 2007.
T. Klinder, C. Lorenz, J. von Berg, S. Dries, T. Bulow, and J.
Ostermann, “Automated model-based rib cage segmentation and
labeling in CT images,” Computer Science, vol. 4792, pp. 195, 2007.
J. Lee and A. P. Reeves, “Segmentation of individual ribs from lowdose chest CT”, Medical Imaging: Computer-Aided Diagnosis, Proc.
of SPIE vol. 7624, 76243J, 2010.