Bone Surface Segmentation in Ultrasound Images:

Application in Computer Assisted Intramedullary

Nailing of the Tibia Shaft

Agn`es Masson-Sibut

1,2

, Eric Petit

, Franc¸ois Leitner

, Julien Normand

Amir Nakib

and Jean-Baptiste Pinzuti

Aesculap Research Center, Parc Sud Galaxie, 1 place du Verseau

38432 Echirolles, France

LISSI, Universit´e Paris-Est Cr´eteil, 61 Avenue du G´en´eral de Gaulle

94010 Cr´eteil Cedex, France

Universit´e de Reims Champagne-Ardenne, 51 rue Cognacq Jay

51095 Reims Cedex, France

Abstract. This paper deals with the use of ultrasound images in order to develop

a Computer Assisted Orthopaedics Surgery system. Ultrasounds are easy to use

in the Operating Room (OR), less expensive than other image modalities, and

faster. We present an automatic method to extract anatomical landmarks from

ultrasound images of femoral anterior condyles. The algorithm is based on an ac-

tive contour model that uses an attraction ﬁeld derived from an Euclidian-distance

map. This segmentation process is a part of a global procedure that includes an

interactive determination of the best image that could be chosen in order to obtain

robust bone segmentation. This global procedure has been successfully tested on

11 volunteers.

1 Introduction

Ultrasound (US) images are often used in different images analysis procedures in med-

ical ﬁeld. For example, in cardiology for automatically segmenting and tracking the left

ventricle : using snakes based on a mapping of intensity gradient [11], with a boundary

estimation algorithm using a Bayesian framework [10], using an adaptive version of the

fast marching level set algorithm [15] or developing an artiﬁcial neural network (ANN)

method [2]. Or, it can be used in the detection of breast cancer to distinguish benign

masses from malignant cancerous masses, with a threshold based method [6], a Neural

Network (NN) based method [3] or an expectation-maximization method [13].

When it comes to orthopaedic surgery, it is more difﬁcult to use US images due

to several properties of the ultrasounds. Nevertheless, more and more studies had been

conducted to ﬁnd a robust bone extraction from ultrasound images. It can be used to

struct directly the 3D surface of the bone [16], or to test mechanical properties of bones

Masson-Sibut A., Petit E., Leitner F., Normand J., Nakib A. and Pinzuti J..

Bone Surface Segmentation in Ultrasound Images: Application in Computer Assisted Intramedullary Nailing of the Tibia Shaft.

DOI: 10.5220/0003307600340042

In Proceedings of the 2nd International Workshop on Medical Image Analysis and Description for Diagnosis Systems (MIAD-2011), pages 34-42

ISBN: 978-989-8425-38-6

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

non-invasively [9]. In our case, we want to develop a Computer Assisted Orthopaedics

Surgery (CAOS) dedicated to intramedullary nailing for the reconstruction of tibia in

case of shaft fractures. This implies to help the surgeon to detect some anatomical struc-

tures that have been deﬁned in a preceding clinical part of this research project [12].

Few methods have been developed to extract structures in US images. Foroughi et

al. (2007) [4] developed a dynamic programming method using well known features of

the US images to extract the bone interface but it does not lead to very accurate results

because they assume the bone interface as composed of the brightest pixels. This can

cause errors localization of the bone interface up to 4 mm in depth [7].

Hacihaliloglu et al. (2008) [5] developed a method to segment the bone surface and

to detect fractures in 3D US images using 3D local features. The localization accu-

racy and mean errors in estimating fractures displacement are pretty good. Although,

in CAOS systems, the probe usually used is a 2D probe because of the difﬁculty to

interpret 3D ultrasound images when you are not accustomed to use ultrasounds, and

because of the cost of such a device.

The paper is organized as follow, ﬁrst we explain how we want to use bone interface

segmentation in the development of a CAOS system. Then, the method of segmentation

using active contours will be developed with some results.

2 Segmentation of the Bone Interface

Our ﬁnal goal is to develop a CAOS system to help orthopaedic surgeon to perform

intramedullary nailing in case of treatment of tibia shaft fractures. In case of tibia shaft

fractures, orthopaedic surgeons can use plates, intramedullary nail, or extern ﬁxation

as treatment. When intramedullary nailing is chosen, the surgeon determines the length

and orientation of the leg, only basing himself on its own expertise. Such a decision is

critical.

Once the nail is in place, the assistance system we developed will help the surgeon

to respect the most the anatomy of the patient. To do so, long bones are considered

symmetrically similar [12]. Two 3D models (one for the healthy member and one for

the injured one) are built using some anatomical landmarks located whether by man-

ual pointer or US probe. These landmarks are the two malleolus and the distal site of

fracture for the distal part of models, and the middle of the trochlea, the condylar line

and the proximal site of fracture for the proximal part of models. These one are located

with the leg in full extension. Thus, the tibia is locked regarding to the femur, and we

can use the femoral frame of reference to orientate the tibia. Then, the system guides

the surgeon to ﬁx the fracture such as ﬁnally, the two models ﬁt.

Some of anatomical points are located using a manual pointer because they are near

the skin. It is not the case for the middle of the trochlea, and the condylar line. We use

ultrasound images and we extract these features automatically.

Figure 1 shows an US image of the femoral condyles (the interface between bone

and tissue is highlighted).

The bone interface in the image represents the interface between soft tissue and

femoral anterior condyles, and the shadow is the non-echo zone under the bone inter-

face . Due to the frequency of US in orthopaedics, waves cannot penetrate the bone

50 100 150 200 250 300 350 400 450 500

100

150

200

250

300

350

400

450

500

Shadow

Bone surface

Soft tissue

Fig.1. An ultrasound image of the femoral condyles. We can easily distinguish the bone interface

and the shadow that represents the non-echo zone under the bone.

surface. This shadow feature is very important because it is a constant in US images of

bone. In our method, it is used to initialize automatically the contour. Anothercharacter-

istic about US imaging of the bone is that the bone interface is very bright in the image.

But, because the US waves and echoes propagate like spherical waves, the resolution is

not very good and the interface thickness can reach over than 4mm [7]. According to

Jain and Taylor, it is more likely that the bone interface lies on the top of the ﬁducial

surface. We take that into account when we calculate the distance map.

To be able to compare 3D models of the injured and the healthy tibia, we have to

deﬁne a precise protocol of acquisition for the US images. In our case, the surgeon put

the probe just under the patella with a full extension leg. Thus, the US probe is locked

by the patella. Then, the surgeon has to scan the anterior condylar proﬁle and the CAOS

system ﬁnds the image perpendicular to the bone surface and extract the landmarks we

want from it.

2.1 Proposed Method

US images have a high level of speckle and intensity dropouts. Thus, to avoid segmen-

tation troubles and to have a continuous contour, we proposed to use an active contour

model. This class of methods was introduced by Kass et al. [8], forces are applied to an

initial curve causing its deformation and displacement until it reaches an equilibrium

state.

The evolution of the snake is based on a minimization of the energy along the curve.

Then, we deﬁne the total energy E

snake

as:

snake

int

(i) + E

ext

(i) (1)

The internal energy (E

int

) is derived from the properties of the curve and is deﬁned by

50 100 150 200 250 300 350 400 450 500

100

150

200

250

300

350

400

450

500

(a)

50 100 150 200 250 300 350 400 450 500

100

150

200

250

300

350

400

450

500

(b)

Fig.2. Movement of the snake. (a) Initial contour (b) Stabilized snake.

int

= α(s)kν

(s)k

+ β(s)kν

(s)k

(2)

where α(s) controls the tension of the curve (the curve acts like a membrane), β(s)

controls the rigidity, and ν(s) = (x(s), y(s)) with s the curvilinear abscissa. In this

paper, we propose a new expression of the external energy that is more adapted to our

application. This energy is based on constrained euclidian map transform.

Firstly, we propose to apply a Derivative of Gaussian (DoG) ﬁlter to the original

image

grad

= ∆(G

∗ I) (3)

Then, we threshold the cumulative histogram of the intensities ( I

grad

), and keep only

the 3% highest values.

= H

cumul

grad

) (4)

At this step, we have a binary image. The following step consists to use a com-

bination of morphological operations to close the area where there are some gradient

points. Morphological ﬁlters are whether erosion (ǫ

) or dilatation (δ

) with a struc-

turing element E which is a binary mask. These ﬁlters can be combined to give closure

(I •E = ǫ

(I)) or opening (I ◦E = δ

(I)). In our case, we perform two closures

with oblique lines to close the two condylar slopes, and to close the logical sum with a

disk element.

Mask

= ((I

• E

(line,85,155)

) ∨ (I

• E

(line,85,25)

)) • E

(disk,15)

(5)

Thus, for line elements, the second argument is the length, and the third is the orienta-

tion. For the disk element, the second argument is the radius. The result image I

Mask

is a binary mask. Afterwards, we use this mask to calculate the Euclidian distance map

that attracts the active contour curve on condylar contours.

ext

= dist(I

Mask

) + dist(I

Mask

) (6)

where I

Mask

is the negation of I

Mask

In the next section, we present the 3 step procedure that we use. The ﬁrst step is

the initialization of the contour on the image chosen by the user. The second step is the

tracking of the bone interface in a serie of US images, and the automatic choice of the

particular image. Finally, the system extracts the landmarks from this image.

2.2 Snake Initialization

The initialization of the snake is performed on an image chosen by the user, where a

visible interface between soft tissue and bone appears. It is known that there is a black

shadow under the bone surface, we initialize the active segmentation process by placing

a closed curve in this part of the image. Then, we determine the forces to be applied

to the curve so it can move to ﬁt the bone interface. The internal force (E

int

) controls

stiffness and elasticity of the curve. We choose parameters that allows the curve to

move without depending on intensity dropouts (high stiffness) and the evolution is fast

enough (high elasticity).

In order to deﬁne the external force, we calculate a constrained Euclidian distance

map on a binary mask. For that, we perform a rough regional segmentation of the

light part of the image that corresponds to soft tissues. As we explained in previous

part, a smoothed gradient is applied by using a Derivative of Gaussian (DoG) opera-

tor (Fig. 3.b). The resulting image is then thresholded to keep only signiﬁcant contour

points (Fig. 3.c). Finally the rough regional segmentation is obtained by a morphologi-

cal closing of the gradient binary image (Fig. 3.d).

Thus we obtain the E

ext

image calculating the Euclidian distance transform (Fig-

ure 4.a). Figure 4.b shows the corresponding ﬁeld of attraction of the active model

contour that leads to the ﬁnal result (Fig. 2.d). This ﬁnal contour will serve to track the

bone surface in a serie of images.

2.3 Tracking of the Bone Surface

After the initial detection of the bone interface , the surgeon scans the region of anterior

condyles in order to ﬁnd an optimal cross-section to the bone surface.

In this procedure, for each new image, the contour detection process is the same than

for the initial detection described in the preceding section, except that the initialization

of the active curve is realized using the last detected bone surface. Then, to assist the

surgeon in this localization, we sum the intensities along the newly detected contour for

each new acquired image ; the maximum value is obtained for the optimal cross-section

(Fig. 5).

Then, we extract landmarks we are interested in from the result image.

2.4 Finding Particular Landmarks

The landmarks the system has to extract are the middle of the trochlea, and top of both

anterior condyles to deﬁne the condylar line. This line allows us to orientate the 3D

model of the tibia.

Due to parameters we used for our snake, we can ﬁnd our landmarks on local maxi-

mum for both top of condyles,and local minimum for the middle of the trochlea (Fig. 6).

50 100 150 200 250 300 350 400 450 500

100

150

200

250

300

350

400

450

500

(a)

50 100 150 200 250 300 350 400 450 500

100

150

200

250

300

350

400

450

500

(b)

50 100 150 200 250 300 350 400 450 500

100

150

200

250

300

350

400

450

500

(c)

50 100 150 200 250 300 350 400 450 500

100

150

200

250

300

350

400

450

500

(d)

Fig.3. Determination of a mask image (a) initial image, (b) ﬁltered image by a Derivative of the

Gaussian, (c) thresholded and ﬁltered image (d) mask image resulting from a closing operation

applied on the binary image.

50 100 150 200 250 300 350 400 450 500

100

150

200

250

300

350

400

450

500

(a)

50 100 150 200 250 300 350 400 450 500

100

150

200

250

300

350

400

450

500

(b)

Fig.4. Field of attraction using to calculate external energy (a) Euclidian distance map used to

calculate the ﬁeld of attraction for the snake (b) Initial image with the ﬁeld of attraction/repulsion

superimposed.

3 Results

The algorithm has been tested on 36 series of images for 11 healthy femurs. First results

demonstrate the validity of the global procedure. The populatio used to test the algo-

rithm is constituted of men and women, from 24 to 40 year-old, and both right and left

knees. Our validation is only qualitative, but 26 series gave a good result. So, 10 series

50 100 150 200 250 300 350 400 450 500

100

150

200

250

300

350

400

450

500

Fig.5. Result of snake evolution.

50 100 150 200 250 300 350 400 450 500

100

150

200

250

300

350

400

450

500

Fig.6. Extraction of Points of Interest from the chosen image.

provide bad results because the acquisition did not follow strictly the protocol. Some

cases, like the image 7.b gives bad result due to the US proﬁle shape. On the contrary,

image 7.a shows a good result.

The execution time is approximately 0.5 second per image. The algorithm has been

implemented on Matlab



, and tested on a computer with a Dual-Core Intel



CPU

(E5200 at 2.50GHz) and 1Go RAM, and the XP SP3 version of Windows



4 Conclusions and Perspectives

We proposed a method to extract the bone surface from US images of the femoral

condyles, and we applied this method in a CAOS system assisting the surgeon per-

forming intramedullary nailing as treatment of tibia shaft fractures. This method can be

used to segment bone surface in other types of images such as for iliac crest, and it can

also be used in some other kind of surgery, like computer assisted osteotomy. In work

under progress is a demonstrator of our CAOS system in the context of an Operating

50 100 150 200 250 300 350 400 450 500

100

150

200

250

300

350

400

450

500

(a)

50 100 150 200 250 300 350 400 450 500

100

150

200

250

300

350

400

450

500

(b)

Fig.7.Some results of landmarks extraction. (a) Good detection of landmarks. (b) False detection.

Room. A use of General-purpose Processing on Graphics Processing Units (GPGPU)

to accelerate calculus is also under progress to be able to run our method on real time.

We also want to extend this work to assist the surgeon on other kind of orthopaedic

surgery (concerning iliac crest for instance).

References

1. D. Amin, T. Kanade, A. M. D. Gioia, and B. Jaramaz. Ultrasound Registration of the Bone

Surface for Surgical Navigation. Computer Aided Surgery, (1):1–16, 2003.

2. T. Binder, M. S¨ussner, D. Moertl, T. Strohmer, H. Baumgartner, G. Maurer, and G. Porenta.

Artiﬁcial neural networks and spatial temporal contour linking for automated endocardial

contour detection on echocardiograms: a novel approach to determine left ventricular con-

tractile function. Ultrasound in medicine & biology, 25(7):1069–76, Sept. 1999.

3. D.-R. Chen, R.-F. Chang, W. Kuo, M. Chen, and Y. Huang. Diagnosis of breast tumors

with sonographic texture analysis using wavelet transform and neural networks. Ultrasound

Medical Biology, 30(5):1301–1310, Apr. 2002.

4. P. Foroughi, E. Boctor, M. J. Swartz, R. H. Taylor, and G. Fichtinger. Ultrasound Bone Seg-

mentation Using Dynamic Programming. 2007 IEEE Ultrasonics Symposium Proceedings,

pages 2523–2526, Oct. 2007.

5. I. Hacihaliloglu, R. Abugharbieh, A. Hodgson, and R. Rohling. Bone segmentation and

fractire detection in ultrasound using 3D local phase features. MICCAI 2008, pages 287–

295, 2008.

6. K. Horsch, M. L. Giger, L. A. Venta, and C. J. Vyborny. Automatic segmentation of breast

lesions on ultrasound. Medical physics, 28(8):1652–9, Aug. 2001.

7. A. K. Jain. Understanding bone responses in B-mode ultrasound images and automatic

bone surface extraction using a Bayesian probabilistic framework. Proceedings of SPIE,

5373:131–142, 2004.

8. M. Kass, A. Witkin, and D. Terzopoulos. Snakes: Active contour models. International

Journal of Computer Vision, 1(4):321–331, Jan. 1988.

9. P. Laugier, F. Padilla, F. Peyrin, K. Raum, A. Saied, M. Talmant, and L. Vico. Current trends

in ultrasonic investigation of bone. ITBM-RBM, 26:299–311, 2005.

10. M. Mignotte, J. Meunier, and J.-C. Tardif. Endocardial Boundary Estimation and Tracking in

Echocardiographic Images using Deformable Template and Markov Random Fields. Pattern

Analysis & Applications, 4(4):256–271, Nov. 2001.

11. A. Mishra. A GA based approach for boundary detection of left ventricle with echocardio-

graphic image sequences. Image and Vision Computing, 21(11):967–976, Oct. 2003.

12. J. Normand. Approche exp´erimentale de la navigation dans les fractures de la jambe. PhD

thesis, Facult´e de M´edecine, Universit´e de Reims, FRANCE, 2009.

13. G. Xiao, M. Brady, J. A. Noble, and Y. Zhang. Segmentation of ultrasound B-mode images

with intensity inhomogeneity correction. IEEE transactions on medical imaging, 21(1):48–

57, Jan. 2002.

14. C. Xu and J. L. Prince. Gradiant Vector Flow : A new external force for snakes. IEEE

Proceedings Conference on Computer Vision Pattern Recognition, pages 66–71, 1997.

15. J. Yan and T. Zhuang. Applying improved fast marching method to endocardial boundary

detection in echocardiographic images. Pattern Recognition Letters, 24(15):2777, 2003.

16. Y. Zhang, R. Rohling, and D. Pai. Direct surface extraction from 3D freehand ultrasound

images. In Proceedings of the conference on Visualization’02, page 52. IEEE Computer

Society, 2002.