Abstract-Our purpose in this study is to segment the rectus abdominis muscle region in X-ray CT images, and we propose a novel recognition method based on the shape model. In this method, three steps are included in the segmentation process. The first is to generate a shape model for the rectus abdominis muscle. The second is to recognize anatomical feature points corresponding to the origin and insertion of the muscle, and the third is to segment the rectus abdominis muscles based on the shape model. We generated the shape model from 20 CT cases and tested the model to recognize the muscle in 20 other CT cases. The average values for the Jaccard similarity coefficient (JSC) and true segmentation coefficient (TSC) were 0.841 and 0.863, respectively. The results suggest the validity of the model-based segmentation for the rectus abdominis muscle

Chisako 

Hiroaki 

Hiroshi

Huayue 

Massayuki 

Naoki Kamiya

Ryujiro 

Takeshi 

Xiangrong 

CiteSeerX

  
 
Abstract—Our purpose in this study is to segment the rectus 
abdominis muscle region in X-ray CT images, and we propose a 
novel recognition method based on the shape model. In this 
method, three steps are included in the segmentation process. 
The first is to generate a shape model for the rectus abdominis 
muscle. The second is to recognize anatomical feature points 
corresponding to the origin and insertion of the muscle, and the 
third is to segment the rectus abdominis muscles based on the 
shape model. We generated the shape model from 20 CT cases 
and tested the model to recognize the muscle in 20 other CT cases. 
The average values for the Jaccard similarity coefficient (JSC) 
and true segmentation coefficient (TSC) were 0.841 and 0.863, 
respectively. The results suggest the validity of the model-based 
segmentation for the rectus abdominis muscle. 
I. INTRODUCTION 
ECENTLY, we are able to obtain high-definition CT 
images rapidly; however, the number of images increases 
to as many as a thousand slices per patient. This causes 
radiologists to overlook areas of no interest on the displayed 
images, when targeting specific diseases. Therefore, we have 
developed the computer-aided diagnosis (CAD) system to aid 
radiologists [1] in recognizing diseases otherwise ignored. In 
particular, in the CT images, the skeletal muscles are not much 
useful to doctors because their interest lies in the internal 
organs that are affected by diseases. Therefore, the effective 
use of image information is expected for the torso CT images, 
which were imaged not for the diagnosis of muscles but for the 
 
Manuscript received March 26, 2011. This research was funded in part by 
a Grant-in-Aid for Scientific Research on Innovative Areas, MEXT, Japan, 
and by the Ministry of Health, Labour, and Welfare under a Grant-In-Aid for 
Cancer Research, Japan. 
Naoki Kamiya is with the Department of Intelligent Image Information, 
Division of Regeneration and Advanced Medical Sciences, Graduate School 
of Medicine, Gifu University, Yanagido 1-1, Gifu 501-1194, Japan (phone: 
+81(58)-230-6515; fax: +81(58)-230-6514; e-mail: kamiya@ 
fjt.info.gifu-u.ac.jp) and also with the Department of Information and 
Computer Engineering, Toyota National College of Technology, 2-1 
Eiseicho, Toyota Aichi, 471-8525, Japan.  
Xiangrong Zhou, Chisako Muramatsu, Takeshi Hara, and Hiroshi Fujita 
are with the Department of Intelligent Image Information, Division of 
Regeneration and Advanced Medical Sciences, Graduate School of Medicine, 
Gifu University, Yanagido 1-1, Gifu 501-1194, Japan. 
Huayue Chen is with the Department of Anatomy, Graduate School of 
Medicine, Gifu University, Gifu 501-1194, Japan.  
Ryujiro Yokoyama and Masayuki Kanematsu are with the Department of 
Radiology Services, Gifu University Hospital, Gifu 501-1194, Japan.  
Masayuki Kanematsu is with the Dept. of Radiology, Gifu University 
School of Medicine and Gifu University Hospital, Gifu 501-1194, Japan.  
Hiroaki Hoshi is with the Department of Radiology, Division of Tumor 
Control, Graduate School of Medicine, Gifu Univ., Gifu 501-1194, Japan. 
other purposes (e.g. diseased organ). Since all torso CT slices 
contain muscle region, we propose an effective use of these 
images.  
The goals of previous researches in the recognition of the 
skeletal muscles in CT images were to develop an anatomical 
database or to assist doctors in surgery. A systematic organ 
visualization method was proposed in the Visible Human 
Project [2] for CT images. This method could be used to 
identify some skeletal muscles, which were manually 
segmented. An atlas-based method for hip muscles was then 
proposed for the surgical planning for artificial joint surgery 
[3]. In this method, the shape model was found useful to 
obtain the region for the muscle. To accomplish the muscle 
recognition in CT, we have been developing a CAD system 
for the skeletal muscles in the torso region and have obtained 
some positive results in the automated recognition of the 
skeletal muscle in the chest and abdominal regions [4-6]. 
However, a recognition method, which considers the 
anatomical shape of the muscles, has not yet been realized 
except for a psoas major muscle region [6].  
In this paper, we propose a novel method to automatically 
segment the rectus abdominis muscle in non-contrast torso 
X-ray CT images for the first time. This method is based on 
generating a shape model for the rectus abdominis muscles 
and applying it to a recognition process. Finally, we estimate 
the Jaccard similarity coefficient (JSC) and true segmentation 
coefficient (TSC) to compare the results of manual extraction 
and this method. 
II. METHODS 
We have already developed the muscle segmentation 
method based on the shape model in the psoas major muscle 
region [6]. The model-based approach was effective for 
segmenting the muscle region with a characteristic shape. The 
psoas major muscle has the spindle shape which we 
approximated its 3-D outer shape. In current method, we focus 
on the cross sectional shape of the rectus abdominis muscle 
and approximate it by using a quadratic function. In this paper, 
the proposed method is based on our advanced scheme; it 
generates the shape model, determines the anatomical feature 
point as landmark (LM), and generates the centerline 
connecting the LMs. Finally, the shape model is applied to 
segment the rectus muscle region. 
A. Shape Model Generation 
The rectus abdominis muscle has a pathognomonic border 
Automated Segmentation of Recuts Abdominis Muscle 
 Using Shape Model in X-ray CT Images 
N. Kamiya, X. Zhou, H. Chen, C. Muramatsu, T. Hara,  
R. Yokoyama, M. Kanematsu, H. Hoshi, and H. Fujita 
R 
978-1-4244-4122-8/11/$26.00 ©2011 IEEE 7993
33rd Annual International Conference of the IEEE EMBS
Boston, Massachusetts USA, August 30 - September 3, 2011
  
shape in its axial transverse section. Figure 1 (a) shows the 
image of the rectus abdominis muscle. The rectus abdominis 
muscle has a long flat shape and is located in front of the body 
[7]. Fig. 1 (b) shows the original CT image, and the arrows 
indicate the rectus abdominis muscle position in the transverse 
section. This section of rectus abdominis muscle displays an 
arching shape. In this method, we focused on its 
cross-sectional shape for model generation. As an initial 
examination, we considered the left and right rectus 
abdominis muscles the same, because of their symmetry. In 
addition, the difference in muscular thickness for different 
transverse sections from its origin to insertion is visually 
imperceptible. Therefore, the shape model of the rectus 
abdominis muscle is defined as the following quadratic 
function that approximates the border shape in the axial 
transverse section: 
y=x2+           (1) 
where y represents the arcate counter-shape in the axial 
transverse section, which is obtained by substituting the 
distance along the centerline for x. For the shape model of 
rectus abdominis, there are two parameters, and . The  is 
a fitting parameter that is automatically determined for each 
case in the following recognition process. Conversely,  is an 
outer shape parameter that corresponds to the thickness of the 
rectus abdominis, and is determined beforehand. The method 
for determining  is described below. 
First, the thickness of the muscle is measured manually in 
twenty training cases. The proposed method has a single 
parameter  for each axial slice; we measure the thickness of 
the muscle from five slices equally spaced in the body axis 
direction. Next, a paired t-test is performed using these values 
to validate that the measured value is suitable for 
approximating the single parameter . Table I shows that there 
was no statistical significant difference in the thicknesses at 
the five slices (p > 0.05). Therefore, 8.0, which was the mean 
value for muscular thickness that was obtained from twenty 
cases, were used for  in expression (1). The model function 
obtained by the above-mentioned procedure is represented by 
the following expression: 
y=x2+8.0           (2) 
Fig. 2 shows the diagram of the proposed method on the 
original CT image. The dotted line describes the shape model 
mentioned above.  
B. Recognition of Anatomical Feature Points 
The process to recognize the landmarks (LM) is based on 
the anatomical positional feature. The anatomical positions 
where the rectus abdominis muscle is connected to skeleton 
are defined as origin and insertion. The correspondence 
between origin and insertion is shown in table II. 
In the LM recognition process, the origin LM is obtained 
from one point on the crista pubica, and the LMs of the 
insertion are obtained from a total of four points located on the 
TABLE I 
THE RESULTS OF PAIRED T-TEST FOR MUSCLE THICKNESS 
RELATIONSHIPS 
CROSS-SECTIONAL PAIR Significance probability 
Slice 1 
Slice 2 0.092 
Slice 3 0.092 
Slice 4 0.945 
Slice 5 0.460 
Slice 2 
Slice 3 0.685 
Slice 4 0.336 
Slice 5 0.748 
Slice 3 
Slice 4 0.194 
Slice 5 0.395 
Slice 4 Slice 5 0.161 
 
 
 
 
 
Fig. 2. Diagram of shape model on the original CT image (dotted line) 
 
 
 
TABLE II 
CORRESPONDENCE BETWEEN ORIGIN AND INSERTION 
Region Origin Insertion 
Rectus abdominis Crista pubica 
Xiphisternum 
Costa V 
Costa VI 
Costa VII 
 
 
   
    (a)            (b) 
Fig. 1. Rectus abdominis muscle (flesh colored and indicated by 
arrows): (a) long flat shape located front of the body, (b) transverse 
section of rectus sheath. The rectus abdominis muscle is enclosed in an 
aponeurotic tendinous sheath [7] 
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xiphisternum and on costa V to costa VII. The targeted 
skeletons are selected from the segmented skeletal image [8], 
and each LM is determined on the selected skeleton.  
C. Centerline Generation 
In the centerline building process, we automatically 
connect the LMs that were determined above. The origin and 
its corresponding insertion are ordinarily connected by the 
shortest possible straight line. However, the shape of the 
abdomen differs greatly according to the body type. Therefore, 
the course of the muscle fiber cannot be simply expressed by 
the straightforward connection of origin and insertion. 
Therefore, a new additional LM that takes into account for the 
individual body shape is dynamically acquired on the basis of 
the LMs that were already set automatically. This process 
takes place as follows. 
Let Lm(x, y, z) be the newly set LM that is defined according 
to the following procedures. Here, the LM of the origin is 
assumed to be Lo(io, jo, ko), and the LMs of the insertion are 
assumed to be Li(il, jl, kl), (l=1, …, 5). First, the z and x 
coordinates of the new LM are determined by the midpoints of 
the origin and insertion.  
         
)(
2
1
lo kkz          (3) 
         
)(
2
1
lo iix           
(4) 
The slice position is determined by expression (3), and the 
position of the surface of the body in the slice section is 
determined by expression (4). Next, the y coordinate is 
determined. The y coordinate that describes the position from 
the surface of the body to the centerline is determined by the 
following expression: 
  ,fat ussubcutaneo,,,
2
1
0;max






 zjxfhjjy (5) 
where h is the size (number of pixels) of the input image in 
anterior-posterior direction, and f is the density of the input 
coordinate. Because of the facility in classifying subcutaneous 
fat from the other tissue using a CT value, the boundary 
coordinates between the subcutaneous fat and rectus 
abdominis muscle can be obtained by expression (5). The 
determined Lm(x, y, z) is assumed to be a midpoint. Therefore, 
three points—the midpoint, the starting point Lo(io, jo, ko), and 
the end point Li(il, jl, kl), (l=1, …, 5) —are connected by a 
straight line to generate the centerline. Fig. 3 shows a 3-D 
image of a skeleton with anatomical centerlines. 
D. Recognition Process 
In the model fitting process, an appropriate fitting 
parameter is obtained from the input image by using the model 
function of expression (2). Further, the area represented by the 
generated function is assumed to be a mask image, from which 
the candidate region is obtained. Finally, the muscle region is 
determined from the peripheral region, if necessary. Details of 
the method of determining the fitting parameter and the 
recognition technique are discussed below.  
In the recognition of rectus abdominis, fitting parameter  
is determined in expression (2). Because the shape model of 
the rectus muscle of abdomen models the cross sectional 
shape, this fitting parameter is determined in each section as 
well. The  can obviously be determined from expression (2) 
by the coordinate pair of x and y. Here, the left and right ends 
of the centerline in each slice (indicated by arrow in Fig. 2) are 
given by Lleft(i, j, k) and Lright(u, v, w), respectively. The value 
calculated by the following expression (6) is substituted for 
expression (2). 















 





 
 0 , 
22
),(
22
vj
j
ui
iyx       (6) 
As a result,  that is appropriate for the test case for 
recognizing the muscle is obtained. This function is assigned 
to the centerline, and the region enclosed by the curve will 
become a mask image (Fig. 4). Finally, the area of the rectus 
abdominis is recognized by using the CT value in this mask 
image. In the mask region, it is comparatively easy to 
recognize the torso tissues by using the CT values, because the 
tissue in the mask region is composed of the muscle, 
subcutaneous fat, and visceral fat regions, which are easy to 
 
Fig. 3. 3-D image of skeleton with anatomical centerlines. Each 
color indicates different anatomical centerlines. Four centerlines on 
each side of rectus abdominis muscle.  
 
Fig. 4. Shape model of the rectus abdominis muscle 
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separate. Fig. 4 shows a mask image generated by the shape 
model function. The two colors represent the masks of the left 
and right sides of the model.  
III. RESULTS AND DISCUSSION 
We applied this scheme to 20 CT cases of non-contrast 
torso X-ray CT images and evaluated the accuracy of the 
segmentation results. The results were evaluated by the 
overlap rate between the recognized and manually extracted 
regions created by an author under the guidance of the 
anatomical specialist. The Jaccard similarity coefficient (JSC) 
[9] and true segmentation coefficient (TSC) [10] were used to 
evaluate the proposed method. The former is calculated by 
   BABA  / , and the latter by   ABA / . A and B are 
the regions of the gold standard and recognition results, 
respectively. The test database consisted of CT images from 
10 males and females. The method was implemented in a 
computer with two 2.99 GHz clock CPUs.  
The average JSC value was 0.841, and the average TSC 
value was 0.863. Fig. 5 shows the recognition results. Here, 
the 2-D recognition results are displayed in the slice images at 
the bottom of the limb section (a) and umbilicus section (b). 
The green region was extracted with our proposed scheme. 
The minimum and maximum value of the JSC was 86.4% and 
92.0%, respectively. The standard deviation of the results was 
6.4%. In the proposed technique, the boundary of the muscle 
was distinguished by using the shape model even if the rectus 
abdominis muscle touches the area of the intestines. The 
results indicate the possibility of using the model for 
automatic recognition of the rectus muscle of abdomen 
extending to abdominal cavity boundary.  
In the proposed method, it can be said that the anatomical 
shape could be reproduced by few parameters. However, in 
order to verify the validity of this method, it is necessary to 
apply this method to database with larger variability. The 
processing time required for the proposed technique was 
about 5 min per case. This reduces the muscle segmenting and 
measuring workload of the radiologist considerably. For 
instance, when the author completely segmented muscle 
manually, 30 min or more were needed. 
IV. CONCLUSION 
We proposed a fully automated method based on the shape 
model to segment rectus abdominis muscle in non-contrast 
torso X-ray CT images. This method was applied to 20 CT 
cases. The average values for the JSC and TSC were 0.841 
and 0.863, respectively. The results were satisfactory in 
generating the muscle shape model for segmenting rectus 
abdominis muscle. However, an improvement in accuracy is 
needed using a large dataset.  
REFERENCES 
[1] X. Zhou, N. Kamiya, T. Hara, H. Fujita, R. Yokoyama, T. Kiryu, and H. 
Hoshi, “Normal anatomical-structure recognitions in high-resolution 
multi-slice CT images,” Proc. of the 10th International Conference on 
Virtual Systems and MultiMedia (VSMM 2004), pp. 75-81, 2004. 
[2] K.H. Hohne, B. Pflesser, A. Pommert, M. Riemer, R. Schubert, T. 
Schiemann, U. Tiede, and U. Schumacher,"A realistic model of human 
structure from the Visible Human data," Meth. Inform. Med. 40, 2, 
pp.83-89, 2001. 
[3] F. Yokota, T. Okada, M. G. Linguraru, R. N. Summers, M. Takao, N. 
Sugano, T. Tada, N. Tomiyama, and Y. Sato, “Application of 
multi-structure statistical atlas to muscle localization from 3D CT data 
of the hip,” The 2nd International Symposium on Computational 
Anatomy, pp. 65-67, 2011. 
[4] X. Zhou, N. Kamiya, T. Hara, H. Fujita, H. Chen, R. Yokoyama, and H. 
Hoshi; Automated segmentation and recognition of abdominal wall 
muscles in X-ray torso CT images and its application in abdominal 
CAD, International Journal of Computer Assisted Radiology and 
Surgery, 2, Supplement 1, S389-S390, 2007. 
[5] N. Kamiya, X. Zhou, H. Chen, T. Hara, H. Fujita, H. Hoshi, R. 
Yokoyama, and M. Kanematsu, "Automated recognition of structure of 
abdominal skeletal muscles from X-ray CT images", Computer 
Assisted Radiology and Surgery 2009, S324-5, 2009. 
[6] N. Kamiya, X. Zhou, H. Chen, T. Hara, H. Hoshi, R. Yokoyama, M. 
Kanematsu, and H. Fujita, “Automated recognition of the psoas major 
muscles on X-ray CT images,” Proc. of the 31st Annual International 
Conference of the IEEE Engineering in Medicine and Biology Society, 
pp.3557-3560, 2009. 
[7] R. Drake, W. Vogl, and A.W. M. Mitchell, “Gray’s anatomy for 
students,” Churchill Livingstone, 2005. 
[8] X. Zhou, T. Hayashi, M. Han, H. Chen, T. Hara, H. Fujita, R. 
Yokoyama, M. Kanematsu, and H. Hoshi, “Automated segmentation 
and recognition of the bone structure in non-contrast torso CT images 
using implicit anatomical knowledge,” Proc. of SPIE Medical Imaging 
2009: Image Processing, 7259, 72593S-1 - 72593S-4, 2009. 
[9] M.R.Anderberg, Cluster Analysis for Applications, Academic Press, 
U.S.A., 1973. 
[10] D. Hori, S. Katsuragawa, R. Murakami, and T. Hirai, “Semi-automated 
segmentation of glioblastoma multiforme on brain MR images for 
radiotherapy planning,” Japanese Journal of Radiological Technology,  
vol. 66, No. 4, pp.353-362, 2010. 
 
(a) 
 
(b) 
Fig. 5. Recognition results (green region) on the original CT image: 
Transverse section at the bottom of the limb (a) and at the umbilicus (b) 
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Automated Segmentation of Recuts Abdominis Muscle Using Shape Model in X-Ray CT Images

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Automated Segmentation of Recuts Abdominis Muscle Using Shape Model in X-Ray CT Images

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