A NEW SEMI-AUTOMATIC APPROACH FOR X-RAY CERVICAL

IMAGES SEGMENTATION USING ACTIVE SHAPE MODEL

Mohammed Benjelloun, Sa

ıd Mahmoudi and Fabian Lecron

Faculty of Engineering, University of Mons, 9 rue de Houdain, Mons, Belgium

Keywords:

Segmentation, Vertebrae, Active shape model, Edges.

Abstract:

This paper describes a new method for cervical vertebra segmentation in digitized X-ray images. We propose a

segmentation approach based on Active Shape Model method whose main advantage is that it uses a statistical

model. This model is created by training it with sample images on which the boundaries of the object of

interest are annotated by an expert. The specialist knowledge is very useful in this context. This model

represents the local statistics around each landmark. Our application allows the manipulation of a vertebra

model. The results obtained are very promising.

1 INTRODUCTION

Nowadays, the radiography of the spinal column is

one of the fastest and the cheapest way for a special-

ist to detect vertebral abnormalities. Furthermore, as

far as the patient is concerned, this procedure has the

advantage to be a safe and non-invasive. For these

reasons, this exam is widely used and remains incon-

testable in the scope of treatments and/or urgent diag-

noses. Despite these precious advantages, a meticu-

lous and tiresome analysis is required by the practi-

tioner. The nature of the radiographs is the origin of

the problem. In fact, they are obtained by impress-

ing the density of the tissues on a radiographic ﬁlm.

A bone is deﬁned by a white color, a soft tissue by a

gray level and an empty space by a black color. It is

a fact that the images present low contrasts and some

areas might be partially hidden by other organs of the

human body. As a result, the vertebra edge is not al-

ways obvious to see or detect.

The problem of vertebra segmentation in digitized

X-ray images is of great importance for the special-

ists. The extraction of quantitative data gives them a

valuable computer-aided diagnosis.

There is a myriad of segmentation methods. Of

course, they are not all recommended for the medi-

cal image processing and all the more so they don’t

all meet the difﬁculties concerning the X-ray images.

The reader is lead to discover (Pham et al., 2000) for

an overview of the current segmentation methods ap-

plied to medical imagery.

The vertebra segmentation has already been

treated in various ways. The level set method is a nu-

merical technique used for the evolution of curves and

surfaces in a discrete domain (Sethian, 1999). The

advantage is that the edge has not to be parameterized

and the topology changes are automatically taken into

account. Some works related to the vertebrae are pre-

sented in (Tan et al., 2006).

The active contour algorithm deforms and moves

a contour submitted to internal and external energies

(Kass et al., 1988). A special case, the Discrete Dy-

namic Contour Model (Lobregt and Viergever, 1995)

has been applied to the vertebra segmentation in (Ben-

jelloun and Mahmoudi, 2008). A survey on de-

formable models is done in (McInerney and Ter-

zopoulos, 1996).

Other methods exist and without being exhaustive,

let’s just mention the generalized Hough transform

(Tezmol et al., 2002), or the use of the polar signa-

ture (Mahmoudi and Benjelloun, 2008).

The difﬁculties resulting from the use of X-ray im-

ages force the segmentation methods to be as robust as

possible. In this paper, we consider a technique based

on the Active Shape Model (ASM) (Cootes et al.,

1995). An active shape model is a statistic model de-

signed from sample of images. This preconception

regarding the shape to search in the image (a verte-

bra in our case) gives the method based on ASM an

important robustness.

We will see that the effectiveness of the method

highly depends on the initialization. The computer-

501

Benjelloun M., Mahmoudi S. and Lecron F. (2010).

A NEW SEMI-AUTOMATIC APPROACH FOR X-RAY CERVICAL IMAGES SEGMENTATION USING ACTIVE SHAPE MODEL.

In Proceedings of the Third International Conference on Bio-inspired Systems and Signal Processing, pages 501-506

DOI: 10.5220/0002765505010506

 SciTePress

aided diagnosis has to fulﬁll the same characteristic

than the radiography technique, i.e. the simplicity and

the rapidity. Therefore, it is crucial to automate and

to provide the results in acceptable times. Extensive

research related to this issue has been done in (Long

and Thoma, 2000; Long and Thoma, 2001).

This paper is structured as follows. In section 2,

we explain the principles of the active shape model

approach and our proposed segmentation method us-

ing this approach. In section 3, the results of the seg-

mentation on data sets of cervical images are com-

mented. Finally, section 4 presents our conclusions.

2 METHOD OVERVIEW

In this paper, we propose a new segmentation ap-

proach based on the active shape model theory. The

goal of this method is to identify vertebra edges

from the cervical spinal X-ray images. An Active

Shape Model is a statistical model that describes ob-

jects shape. Basically, an active shape model (ASM)

(Cootes et al., 1994) is a statistical model generated

from a set of training samples. A series of correspond-

ing points, called landmark points, are identiﬁed on

the boundary of the target object in each training im-

age. Then the training samples are regarded as vec-

tors and statistical parameters of the vector distribu-

tions are computed using principal component analy-

sis. By changing the parameters, new shapes can be

synthesized. After the ASM is trained, it can be used

to locate objects in a new image. The contour ex-

traction process using ASM is a process of synthesiz-

ing an optimal shape that is most similar to the shape

in the image. The statistical difference between the

synthesized shape and the original model can be cal-

culated. By restricting the difference to small values,

the deformation can be limited to an acceptable range.

In the followed section brieﬂy reviews the ASM seg-

mentation scheme.

2.1 Active Shape Model Method

The ASM method is composed of 4 steps (Figure 1):

1. Learning: placing landmarks on the images in or-

der to describe the vertebrae

2. Model Design: aligning all the marked shapes for

the creation of the model

3. Initialization: the mean shape model is associated

with the corners of the searched vertebrae. This

step can be manual or semi-automatic

4. Segmentation: each point of the mean shape

evolves so that its contour ﬁts the edge of the ver-

tebrae

Figure 1: The steps of our framework.

2.1.1 Learning

A set of image samples has to be described by some

landmarks. It is therefore common to choose as land-

marks the corners of the vertebra and a reasonable

number of equidistant points between these corners.

Figure 2 shows an example of this process.

Figure 2: Vertebra marking.

Each shape in the set is represented by a vector x

= (x

, y

;. . . ;x

, y

;. . . ;x

, y

)

(1)

2.1.2 Model Design

When the annotation phase is completed, it is neces-

sary to align the shapes to make a correct statistical

treatment since they are indeed positioned at various

locations and orientations of an X-ray image. The al-

gorithm is as follows:

1. Align each shape of the sample on the ﬁrst one.

2. Repeat until convergence:

(a) Compute the mean shape.

(b) Adjust the mean shape to the ﬁrst shape.

Once the set of aligned shapes is available, the

mean shape is calculated using the arithmetic mean

of coordinates describing each element of the sample

(see equation 2).

x =

∑

i=1

(2)

A set of possible models is derived from this mean

shape by the moving of points through speciﬁc di-

rections called modes of variations. These directions

BIOSIGNALS 2010 - International Conference on Bio-inspired Systems and Signal Processing

502

are equivalent to the eigenvectors of the variance-

covariance matrix of the sample. Finally, the model

is described by the mean shape x , the matrix P of the

most signiﬁcant eigenvectors p

corresponding to the

eigenvalues λ

and a vector b of weight factors b

. We

have:

x = x + Pb (3)

With:

P = (p

, p

, ··· , p

)

b = (b

, b

, ··· , b

)

The equation 3 is used to decide if an object from

an image can be considered as convenient. As the

coordinates of the landmarks of an object are known

and as the eigenvectors are unit vectors, it is possible

to determine the vector b by the equation 4.

b = P

(x − x) (4)

The values of the factors b

allow to know if an

object is convenient to the model. The values of b

can

vary in the following manner (Cootes et al., 1994):

−3

≤ b

≤ 3

(5)

2.1.3 Initialization

For the initialization process, we propose a semi-

automatic process based on two points placed by the

user on the left side of each vertebra on the superior

and inferior corners. The mean shape is positioned

according to this information.

2.1.4 Segmentation

Having generated a ﬂexible shape model, we would

like to ﬁnd examples of the modeled form when it is

present in the images. So, after the initialization step,

shapes are ﬁtted in an iterative manner, starting from

the mean shape.

For each landmark belonging to the mean shape,

it is necessary to analyze the surrounding texture. It

is always important to consider changes in the level

of gray in the same direction to ensure a coherent re-

search. Therefore, it was chosen to analyze the tex-

ture around landmarks along the normal of the con-

tour at that point (see Figure 3). Thus, a proﬁle is de-

ﬁned as a vector containing the gradient of intensity

for each point in the normal. Each landmark is moved

to the direction perpendicular to the contour to n

po-

sitions on either side, evaluating a total of 2n

+ 1 po-

sitions. We can notice that these positions correspond

to the proﬁle of each landmark on the mean shape.

In our experiments, we chose n

= 7. The landmark

Figure 3: Normal of the contours for each point of the pro-

ﬁle.

is moved to the position with the lowest Mahalanobis

distance (Cootes et al., 1995). After moving all the

landmarks, the shape is ﬁtted to the displaced points

(by respecting the equation 5), yielding an updated

segmentation.

The search algorithm is given here:

Do:

• Search, along each normal of the shape, the best

proﬁle according to the computed mean shape.

The new sections are landmarks and proﬁles

found.

• Search shape model best suited to points found in

the previous step. This will form the basis for the

next iteration.

While the convergence condition is not met and

the maximum number of iterations is not reached.

It remains to determine when to suspend the con-

duct of the search algorithm previously presented.

The ﬁrst condition of convergence proposed is to

stop the search when all the landmarks remain ﬁxed.

However, it appears that this condition is too strict.

We have therefore decided to stop searching when a

small percentage of the landmarks continues to move.

Speciﬁcally, we compare the shape obtained in the

current iteration with all the forms built with previ-

ous iterations. For each of them, we compute the

number of points that differ from those recently ob-

tained. We then seek the minimum of these values. If

the corresponding shape is close to the present shape,

we then compare the number of points that differ be-

tween these two shapes. If the ﬁrst value is less than

10% of the second, the convergence condition is met.

To avoid an indeﬁnite search if the vertebrae are not

A NEW SEMI-AUTOMATIC APPROACH FOR X-RAY CERVICAL IMAGES SEGMENTATION USING ACTIVE

SHAPE MODEL

503

found, a maximum number of iterations can be ﬁxed.

If this number is reached, the search ends and the re-

sult of the current iteration is proposed as a ﬁnal so-

lution. In practice, when an initialization is done cor-

rectly, the method converge after 50 to 250 iterations.

3 EXPERIMENTAL RESULTS

We proposed and developed a segmentation method

based on the active shape model theory. Our goal was

to produce a tool for vertebra detection in X-ray im-

ages corresponding to the cervical spinal column. We

validated our method by using a test database com-

posed of more than 10 000 X-ray images from the

online database NHANES II of the National Library

of Medicine (NLM, ). Our application was developed

in order to allow the use of a vertebra model. Figure 5

shows the segmentation results obtained by this kind

of modelization.

We study in our experiments the inﬂuence of some

parameters in the ﬁnal segmentation result, such as

the number of sample images, the proﬁle structure and

the number of landmarks by vertebra used to deﬁne

the mean shape model.

For bothly a powerful and useful segmentation,

the choice of images sample should be the task of

a specialist. The dataset size recommended for the

training set varies from one database to another. Nev-

ertheless, the larger the sample, the best the built

model. We proposed a sample composed of 25 im-

ages for our tests. This number provided good seg-

mentation results. It is obvious that this number can

be augmented, but by increasing the mean shape com-

puting time.

By the same way, the number of landmarks has

a direct inﬂuence on the quality of the segmentation

results obtained by the search process. It is evident

that the greater this number, the better the segmen-

tation result. Nevertheless, it would be necessary to

ﬁnd a good compromise, in order to obtain a reason-

able computing time for the search phase process. We

carry out this compromise by using 20 landmarks for

each vertebra.

The last parameter inﬂuencing the segmentation

results is the structure of the proﬁles used for the

search process phase. This one depends on two pa-

rameters: the number of points by proﬁle and the dis-

tance between these points. We can also notice that

to ensure an independence of this spacing with re-

spect to the image size, this distance is proportional

to the vertebra size. After various tests, we conclude

that a proﬁle of seven points spaced by a distance

equal to 5% of the vertebra size is a good compro-

(a) Image 1. (b) Image 2.

Figure 4: Test images.

Table 1: Vertebra recognition rate.

Vertebra Type Recognition Rate

C3 96%

C4 98%

C5 96%

C6 98%

C7 86%

mise. Figure 5 shows the segmentation results for the

three images corresponding to the cervical spinal col-

umn (Figure 4) on the basis of the parameters pre-

sented above. After convergence, all the vertebrae are

detected perfectly. The segmentation results for the

chosen images tests show that vertebra edges are de-

tected perfectly by applying the proposed segmenta-

tion approach, based on a vertebra model and using

the Active Shape Model approach. The Table 1 pro-

poses the vertebra recognition rate of our method on

50 images.

BIOSIGNALS 2010 - International Conference on Bio-inspired Systems and Signal Processing

504

(a) Image 1.

(b) Image 2.

Figure 5: Segmentation results.

4 CONCLUSIONS

The goal of this paper was to present a semi-automatic

technique applied to cervical vertebra edge detection

in X-ray images. To this aim, we used a segmentation

approach based on Active Shape Model. This method

is composed of two stages: a stage of modeling and

another of search. We proposed an approach which

consists on modeling all the shapes of vertebrae by

only one vertebra model. The multiple tests which

we carried out on a large dataset composed of varied

images prove the effectiveness of the suggested ap-

proach. We can also notice that the proposed method

allows a fast contours extraction and is more repro-

ducible than the manual method. This method can be

adapted to other component of the spinal column: like

dorsal or lumbar.

The principal inconvenient of this ASM based

segmentation approach is the stage of training, which

is time consuming. Another important problem of this

approach is the impact of pose initialization in ASM:

the closer the mean shape is placed to the actual ob-

ject, the better the chances of having a successful seg-

mentation are. In our case, we solve this problem by

proposing a semi-automatic approach. So, we suggest

to place the mean shape model on the image by using

the vertebra left corners edges which are placed by the

user. This approach produces a very good initializa-

tion of the search process.

In our future works we want to investigate a

method aiming to propose an automatic approach of

segmentation. To this end, we can use some corner

detectors. We also consider the use of these segmen-

tation results in order to analyze the mobility of the

cervical spinal column. Another perspective of our

work consists in using the Graphics Processing Unit:

GPU, in order to accelerate the process of mean shape

calculation, and also the ASM search stage.

REFERENCES

Benjelloun, M. and Mahmoudi, S. (2008). X-ray image

segmentation for vertebral mobility analysis. Inter-

national Journal of Computer Assisted Radiology and

Surgery, 2(6):371–383.

Cootes, T. F., Hill, A., Taylor, C. J., and Haslam, J. (1994).

Use of active shape models for locating structures

in medical images. Image and Vision Computing,

12(6):355–365.

Cootes, T. F., Taylor, C. J., Cooper, D. H., and Graham, J.

(1995). Active shape models: Their training and ap-

plication. Computer Vision and Image Understanding,

61(1):38–59.

Kass, M., Witkin, A., and Terzopoulos, D. (1988). Snakes:

Active contour models. International Journal of Com-

puter Vision, 1(4):321–331.

Lobregt, S. and Viergever, M. (1995). A discrete dynamic

contour model. IEEE Transactions on Medical Imag-

ing, 14(1):12–24.

Long, L. R. and Thoma, G. R. (2000). Use of shape models

to search digitized spine x-rays. In Proceedings of the

13th IEEE Symposium on Computer-Based Medical

Systems, pages 255–260.

A NEW SEMI-AUTOMATIC APPROACH FOR X-RAY CERVICAL IMAGES SEGMENTATION USING ACTIVE

SHAPE MODEL

505

Long, L. R. and Thoma, G. R. (2001). Identiﬁcation and

classiﬁcation of spine vertebrae by automated meth-

ods. In Proceedings of SPIE Medical Imaging 2001,

volume 4322.

Mahmoudi, S. and Benjelloun, M. (2008). A new approach

for cervical vertebrae segmentation. Progress in Pat-

tern Recognition, Image Analysis and Applications,

4756:753–762.

McInerney, T. and Terzopoulos, D. (1996). Deformable

models in medical image analysis: A survey. Medi-

cal Image Analysis, 1(2):91–108.

NLM. National library of medicine : Nhanes ii

project database. http://archive.nlm.nih.gov/

proj/dxpnet/nhanes/nhanes.php.

Pham, D. L., Xu, C., and Prince, J. L. (2000). Current meth-

ods in medical image segmentation. Annual Review of

Bimedical Engineering, 2:315–337.

Sethian, J. A. (1999). Level Set Methods and Fast Marching

Methods. Cambridge University Press.

Tan, S., Yao, J., Ward, M. M., Yao, L., and Summers, R. M.

(2006). Level set based vertebra segmentation for

the evaluation of ankylosing spondylitis. Progress in

biomedical optics and imaging, 7(1)(30):12–24.

Tezmol, A., Sari-Sarraf, H., Mitra, S., Gururajan, A., and

Long, R. (2002). Customized hough transform for ro-

bust segmentation of cervical vertebrae from x-ray im-

ages. In Proceedings of the 5th IEEE Southwest Sym-

posium on Image Analysis and Interpretation, page

224.

BIOSIGNALS 2010 - International Conference on Bio-inspired Systems and Signal Processing

506