REAL-TIME 3D FILTERING OF ULTRASOUND DATASETS

Dong-Soo Kang, Koojoo Kwon, Eun-Seok Lee, Sang-Chul Lee and Byeong-Seok Shin

Dept. of Computer & Information Engineering, Inha Univ., 253 Yonghyun-Dong, Nam-Gu, Inchon, Korea

Keywords: Interactive visualization, GPU-based volume ray-casting, Ultrasound data rendering, 3D filtering.

Abstract: Ultrasound imaging is used in a variety of medical areas. Although its image quality is inferior to that of CT

or MR, it is widely used for its high speed and reasonable cost. However, it is difficult to visualize ultra-

sound data because the quality of the data might be degraded due to artifact and speckle noise. Therefore,

ultrasound data usually requires time-consuming filtering before rendering. We present a real-time 3D filter-

ing method for ultrasound datasets. Since we use a CUDA

technology for 3D filtering, we can interac-

tively visualize a dataset. As a result, our approach enables interactive volume rendering for ultrasound

datasets on a consumer-level PC.

1 INTRODUCTION

Ultrasound imaging is a well-known diagnosis

method to visualize the distribution of ultrasonic

echo signal. Although its image quality is worse than

those of CT and MR images, it is widely used for

diagnosis for its cost-effectiveness. An ultrasound

device has several advantages compared with other

medical imaging modalities. It is regarded as being

relatively safe (Dunn, 1991), it involves no ionizing

radiation, and most examinations are noninvasive

and do not distress patients (Kuo, 2007). In addition,

the acquisition procedure is faster than those of other

medical imaging techniques. And ultrasound offers

interactive visualization of the underlying anatomy

with the ability to represent dynamic structures.

However, we have to consider two major problems

of the 3D ultrasound visualization: lower signal-to-

noise ratio and the fuzzy nature of the boundary sur-

faces in the ultrasound image.

In order to reduce the noise, lots of methods

have been proposed (Fattal and Lischinski, 2001).

During visualization of ultrasound data, the filtering

stage is very time consuming since most of filtering

methods refer to the entire voxels of volume dataset,

and they are executed on CPU using MultiMedia

eXtension (MMX) or Open Multi-Processing

(OpenMP) technology.

In this paper, we present a real-time 3D filtering

for ultrasound datasets using graphics hardware. We

use CUDA

(short for Compute Unified Device

Architecture) from nVidia to program inexpensive

multi-thread GPUs. Also, we can perform coordinate

conversion between ultrasound and Cartesian coor-

dinates using fragment shader. While most of filter-

ing methods takes long time, our method performs

filtering in real-time with modern graphics hard-

ware. It helps doctors to diagnose patient with inter-

active operation.

In Section 2, we briefly review previous work,

and our method is explained in Section 3. In Section

4, the experimental results are presented, and

Section 5 gives the conclusion and future work.

2 RELATED WORK

Ultrasound data are acquired in near real-time. So,

the data manipulation and visualization have to be

processed as fast as data acquisition. In the data ma-

nipulation process, since ultrasound data typically

contains speckle noise and fuzzy boundaries, we

have to remove them. Sanches et al. described sev-

eral techniques to improve the efficiency of the sur-

face reconstruction principles in Taylor series (João

and Sanches, 2003). However it takes long process-

ing time. Kim et al. proposed filtering method using

truncated-median filter in 2D ultrasound image (Kim

and Oh, 1999), which requires pre-processing stage.

Burckhardt presented a theoretical analysis of the

noise such as an interference phenomenon (Burck-

hardt, 1978). The analysis is based on an object that

comprises many point scatterers per resolution cell,

with a random phase associated with each scattered

echo. A number of researchers (Coppini and Poli,

473

Kang D., Lee E., Kwon K., Lee S. and Shin B. (2010).

REAL-TIME 3D FILTERING OF ULTRASOUND DATASETS.

In Proceedings of the Third International Conference on Health Informatics, pages 473-476

DOI: 10.5220/0002757704730476

 SciTePress

1995) use Gaussian weighted averaging to smooth

images and the Laplacian-of-Gaussian (LoG) to de-

tect tissue boundaries (Torre and Poggio, 1986).

Evans and Nixon have proposed using a variation of

the median filter to estimate the local mode within

ultrasound images (Evans and Nixon, 1993). It is

difficult to determine the mode within a small popu-

lation such as 9×9 filter kernel. Consequently, they

use a truncated median filter to estimate the mode of

the local distribution. Although these methods are

good for remove noise, these are unsuitable for real-

time system.

3 REAL-TIME 3D FILTERING

USING CUDA

Figure 1 shows the overview of our method. Firstly,

the volume data acquired by ultrasonic probe is

transferred to the filtering module using CUDA

Every voxel is allocated in each thread on GPU.

Then filtered data is transferred to the rendering

module. Finally, the result image is generated by

using volume ray casting on fragment shader.

Figure 1: Overall procedure of our method.

3.1 Conversion between Ultrasound

and Cartesian Coordinates

To obtain high-quality images at interactive speed,

we exploit volume ray casting (Krüger and Wester-

mann, 2003). Because this computes the volume-

rendering integral along rays, it samples scalar val-

ues in the Cartesian coordinate. Unlike CT and MR

data acquired as 3D Cartesian grid form, ultrasound

data is more complicated, because typical ultrasound

data involves non-Cartesian grids. As a result, time-

consuming coordinate conversion between the ultra-

sound and Cartesian coordinates is required.

The upper two images in figure 2 depict 3D ultra-

sound volume data, and the others are the cross-

sectional images of the 3D ultrasound volume data

on the YZ plane and the XZ plane, where the X, Y,

and Z axes are in Cartesian coordinates. We generate

a 3D ultrasound volume dataset by stacking 2D im-

ages acquired via a probe scan (see Fig. 2 (top)). We

assume that

is the angle in the 2D image (scan

viewing angle) and that

is the angle for generating

an ultrasound 3D volume dataset (probe viewing

angle). The ranges of

and

are 0° ≤

< Ω < 180°

and 0° ≤

< Φ < 180°, where Ω and Φ are the full

ranges scanned by ultrasound probe for the

and

respectively. The value r is the distance from the

probe to a position of interest, the a is the distance

between the origin and the maximum probe viewing

angle, and b means the distance to the start position

to collect voxel values in 2D images

Figure 2: Ultrasound coordinates (top two images) and YZ

and XZ cross-sectional plane (bottom two images).

A data from the ultrasound probe is represented in

ultrasound coordinates, having been converted to

Cartesian coordinates. This means that each position

in ultrasound coordinates has to be changed into a

position in Cartesian coordinates. Then we can ob-

tain the rendered image using these converted sam-

pling positions. We calculate this conversion be-

tween the two coordinates via equation (1).

bRxr

azyR

−+=

⎟

⎠

⎞

⎜

⎝

⎛

⎟

⎠

⎞

⎜

⎝

⎛

−+=

−

tan

(1)

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3.2 Shape of Filters

We have to determine the size of smoothing filter

according to voxel position and voxel value. We

propose two methods, region-based 3D filtering and

OTF-based 3D filtering.

3.2.1 Region-based 3D Filtering

In the clinic applications, we can set the region of

interest (ROI) on the ultrasound dataset as shown in

figure 3. We divide a space into inside and outside

region. Then we apply different filters according to

region types : small-sized filter kernel (e.g. 2

or 3

average filter) for inside and large-sized filter kernel

(e.g. 5

or 9

average filter) for outside. It is helpful

to improve visibility since we can exclude less-

significant region. We can apply average filter n

times in outside region. The filtered region is getting

blurred image while the value of n grows up. It

means that region is reduced a noise from the final

result. Since this procedure is performed so fast, our

system can be operated interactively whether the

outside is interesting region or not.

We set the ROI manually or semi-automatically.

We consider the opacity transfer function (OTF)

when we use a semi-automatic method. The values

of the minimum and the maximum against each axis

are used to set the ROI boundary. All procedure is

executed parallel assigns one voxel by one thread.

Figure 3: Inside and outside regions of ROI. Since we

remove fuzzy structure by applying large-sized filters, we

can focus on the interested object.

3.2.2 OTF-based 3D Filtering

OTF is used to determine whether an object belongs

to interesting region or not. We apply the different

filter kernel for each voxel according to an OTF

value. We decide a filter kernel size after comparing

the density value of each voxel and its correspond-

ing OTF value. Figure 4 shows the different filter

kernel used in filtering stage. If the voxel value is in

the threshold (see interesting object in figure 4), we

apply the small-sized filter to it, otherwise (see non-

interesting object in figure 4) we set the large one

for the voxel. During filtering stage, a variety filter

types are applicable such as average filter and Gaus-

sian filter without additional cost.

Figure 4: OTF-based 3D filtering method. The different

filter kernel is applied to each voxel during the filtering

stage.

4 EXPERIMENTAL RESULTS

We tested our method on a PC equipped with an

AMD

Athlon dual-core processor 4200 (2.21

GHz) with 2 GB main memory. The graphics accele-

rator is an NVIDIA 7800 GTX

(with 256 MB

video memory). We used the DirectX 9.0 and the

High-Level Shading Language shader Version 3.0.

We evaluated filtering speed and the quality of

resulting images. Table 1 shows the filtering speed

of CPU-based method and CUDA

-based one. Our

approach much is faster than the conventional CPU-

based method. When we compared the filtering time

of region-based method with that of OTF-based

method while applying the same filter kernel, we

found that the OTF-based method is approximately

15% faster than region-based method because there

is more filtering instruction on outside region. When

we apply both OTF- and region-based method, the

speed of filtering is a little decrease since there is

more instruction set in every thread.

Table 1: Comparisons of filtering speed (fps).

Dataset

CPU-

based

CUDA

-based

OTF-

based (A)

Region-

based (B)

(A)+(B)

256×256×51 < 1 42 38 35

256×256×96 < 1 22 18 16

Figure 5 shows quality of the resulting images. Top

row images are rendered by using ray casting when

filtering on CPU and bottom row images are ren-

dered with our method. We can verify the same

quality of both images. In Figure 6, when we applied

region-based method, non-interesting objects in out-

side of volume are removed efficiently. We applied

filter kernels 8 times on the outside region. So, we

can verify that fuzzy structure and a part of placenta

are removed.

REAL-TIME 3D FILTERING OF ULTRASOUND DATASETS

475

Figure 5: Result images of our method. CPU-based

method (top row) and CUDA

-based method (bottom

row). Images from row data (left), Images when applying

average filter (middle) and 5

average filter (right).

Figure 6: Comparison of images with region-based me-

thod. Result image without region-based method (left) and

with our method (right). Some fuzzy structures are re-

moved efficiently when we use our method.

5 CONCLUSIONS AND FUTURE

WORK

3D visualization of ultrasound data is difficult be-

cause the quality of the data might be degraded by

artifact and speckle noise. We present a real-time 3D

filtering for ultrasound datasets using CUDA

technology. We can interactively visualize an ultra-

sound dataset from the data gathering stage to the

rendering stage. The fast processing of ultrasound

data is good for diagnosing patient interactively. As

a result, our approach enables interactive visualiza-

tion of ultrasound datasets. It is helpful that doctor

can manipulates ultrasound system dynamically and

find optimal information interactively.

In the future we plan to build up the integrated

ultrasound system with GPU for interactive applica-

tion. This system will be included with interactively

ultrasound data acquisition module from prove, real-

time ultrasound rendering module using GPU, filter-

ing module using our method and efficient GUI

(Graphical User Interface) for medical staff. Addi-

tionally, we would like to investigate the integration

of our interactive approach with methods for auto-

mated noise detection.

REFERENCES

Burckhardt, C., 1978. Speckle in ultrasound B-mode

scans. In IEEE Transactions on Sonics and Ultra-

sonics.

Coppini, G., Poli, R., Valli, G., 1995. Recovery of the 3-D

shape of the left ventricle from echocardiographic

images. In IEEE Transactions on Medical Imaging.

Dunn, F., 1991. Ultrasound. In IEEE Transactions on

Education.

Evans, A., Nixon, M., 1993. Speckle filtering in ultra-

sound images for feature extraction. In International

Conference on Acoustic Sensing and Imaging ′93.

Fattal, R., Lischinski, D., 2001. Variational classification

for visualization of 3D ultrasound data. In Proceed-

ings of Visualization ′01.

João, M., Jorge, S., 2003. A 3D Ultrasound system for

medical diagnosis. In Pattern Recognition and Image

Analysis, First Iberian Conference IbPRIA 2003.

Springer Verlag LNCS Press.

Kim, C., Oh, J., Park, H., 1999. Efficient volume visuali-

zation of 3-D ultrasound image. In Proc. SPIE Medi-

cal Imaging.

Kuo, J., Bredthauer, G., Castellucci, J., Von Ramm, O.,

2007. Interactive volume rendering of real-time three-

dimensional ultrasound images. In IEEE Transaction

on Ultrasonics, Ferroelectrics and Frequency Control.

Krüger, J., Westermann, R., 2003. Acceleration techniques

for GPU-based volume rendering. In Proceedings of

the 14th IEEE Visualization ′03.

Levoy, M., 1988. Display of Surface from volume data. In

IEEE Computer Graphics and Applications.

Torre, V., Poggio, T., 1986. On edge detection. In IEEE

Transactions on Pattern Recognition and Machine

Intelligence.

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