Proceedings of the Second APSIPA Annual Summit and Conference, pages 276–279,Biopolis, Singapore, 14-17 December 2010.

Comparisons of Centerline Extraction Methods ForLiver Blood Vessels in ImageJ and 3D Slicer

Zhimin Wang∗, Yanling Chi†, Weimin Huang∗, Sudhakar K. Venkatesh‡,Qi Tian∗, Thiha Oo∗, Jiayin Zhou∗, Wei Xiong∗ and Jimin Liu†

∗ Institute for Infocomm Research, 1 Fusionpolis Way

Coonexis #21-01, Singapore, 138632

E-mail: zwang@i2r.a-star.edu.sg† Biomedical Imaging Laboratory

Agency for Science, Technology and Research, Singapore, 138671‡ Department of Diagnostic Radiology

Yong Loo Lin Schooll of Medicine

National University of Singapore, Singapore, 119074

Abstract—In this paper, we introduce the centerline extractionprocesses of the liver vessel systems by using the plug-ins inImageJ and 3D Slicer. The Skeletonize (2D/3D) plug-in in ImageJis easy to use, and only based on the volumetric data. It performsfast and require no user interactions in terms of seed pointsselection. The VMTK toolkit in 3D slicer is more powerful.It uses the surface model and can provide more smooth andconsistent results. Due to its complexities, it requires morecomputational resources and user specified seed points. Somepossible improvements for these two methods are discussed.

I. INTRODUCTION

Computer-aided diagnosis (CADx) and computer-assisted

surgery (CAS) have received great attentions in the last

two decades in medical image analysis. Theses systems are

designed to help the radiologist or surgeons to analyze and

evaluate the massive information obtained via different imag-

ing techniques like X-ray CT, ultrasound, and MRI. Based on

the analytic results, the follow up clinical management steps

can be decided and applied.

Liver vasculature analysis, as one of the applications for

CADx and CAS, is an important part in image analysis for

liver. Accurate, reliable, and robust segmentation of the liver

vasculature is essential for many clinical applications such

as quantitative diagnosis, preoperative planning, and surgical

simulation, as well as the longitudinal analysis of vascular

disease [1]. For example, in the liver transplant operation,

3D vessel tree visualization and the branching patterns of the

vessel system is extremely useful for the doctors to decide

whether a person is suitable as a donor [2]. Also for patients

with liver cancer or tumors, the spatial location of the tumors

in relation to the major vessels determine the extend of

oncologic resections [3].

Contrast-enhanced liver CT image is the most commonly

used CT image for the analysis of liver vessel systems. In order

to increase the contrast between liver and vessels, contrast

agents are injected into the bloodstream to increase the vessel

opacity, which will make vessels brighter than the surrounding

structures. Give a volume of these contrast-enhanced CT

images, the vessels can be extracted through three general

classes of approaches [4]: 1) intensity based algorithms, 2)

model based algorithms, and 3) centerline-based approaches.

Some other techniques and algorithms can also be found in

[5]. By using these methods, we can obtain a good localized

approximation of the vessel trees and their 3D visualization.

The vessel segmentation results can be further processed for

image processing tasks like registration, anatomical partition,

branching and visualization [6]. Sometimes, skeletonized the

vessel trees is more useful for these applications. The cen-

terline representation is simpler than the volume or surface

rendering, while preserving the topology of the whole vessel

trees. Combining the centerline and the diameter information

of the vessels, we can rebuild and generate better visualizations

of the live vasculature.

Many centerline extraction approaches for 3D vessel system

have been proposed in the literature recently. They can be di-

vided into the following 4 groups [7], the topological thinning

method [8], the distance transform-based method [9], [10],

the methods based on wave propagation idea [11], and the

extraction methods using Voronoi diagram [13]. Some of the

methods have been integrated into the medical image analysis

toolkits, for example, the 2D/3D skeletonize plug-in in ImageJ

[14], and VMTK extension in 3D slicer [16]. There are some

other toolkit providing the centerline extraction function as

well, such as the CMIV CTA plug-in in OsiriX [15]. However,

it is not an independent functionality. In this study, we want

to compare the above two centerline extract methods on the

segmented vessel volume data sets in our liver workbench

system, in order to get a good understanding on the advantages

and limitations of them.

Rest of the paper are organized as follows. In Section II, we

will briefly introduce the three centerline extraction methods

which are of interests. The comparison results will be given

in Section III. Finally, the paper is concluded in Section IV.

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(a) (b) (c)

Fig. 1. The process of centerline extraction using ImageJ. (a) The input volume data (b) Skeletonize (2D/3D) plug-in in ImageJ. (c) The output centerlines.

II. DATASETS AND METHODS

A. Segmented vessel datasets

We extract the liver vessel systems from the CT images

using a shape context based voting method which is currently

under review. It is basically a shape information guided vessel

segmentation/grouping method, and segmentation output is in

the form of 3D volume. The CT images used in our studies

are acquired using a standard four-phase contrast-enhanced

imaging protocol, with resolution of 512x512 pixels, and of

1mm or 3mm thickness.

B. 2D/3D skeletonize plug-in in ImageJ

The Skeletonize (2D/3D) plug-in in ImageJ is based on

the thinning algorithm proposed by Lee et al. [14]. This

method utilizes the medial surface/axis thinning method to

obtain the centerlines of the object. In their method, the Euler

connectivity and medial surface/axis are used as topological

and geometrical constraints. With these constraints, the skele-

tonized results are smooth, invariant to topological variations,

and less sensitive to boundary noise.

In order to use this function in ImageJ, the 2D or 3D input

image data must be 8-bit, grayscale, and binarized. It works on

planar image or 3D volume data. The resulting skeleton image

is a binary image with intensity value 255 for the skeleton

components and 0 for the background pixels/voxels. Within

the whole process, there is no need to select seed points. Fig.

1 shows a simple process of how to use ImageJ to extract the

centerlines from the volumetric vessel data.

C. VMTK in 3D Slicer

The Vascular Modeling Toolkit (VMTK) is an extension

of the 3D slicer, a free open source software package for

medical image analysis. It offers a series of sophisticated func-

tionalities, such as segmentation, centerline extraction, branch

splitting, geometric analysis, and mapping and patching, for

the analysis of blood vessels. It is built on top of ITK and

VTK, and the VMTK package can also be used independently.

Unlike the plug-ins in ImageJ, the input image data required

by VMTK is its polygonal surface representation. Before the

centerline extraction step, a polydata model of the vessel

volume data must be constructed, and then the input mesh

model will be closed with caps. After the model is prepared,

starting seeds and target seeds are needed to be specified. The

centerline are computed by using the Voronoi diagram [16] of

the input surface model.A typical centerline extraction process by using VMTK

can consists of the following steps: 1) Construction of a 3D

polydata model. 2) After that, the polydata model is further

processed to close the holes in the surface. 3) Perform the

centerline extraction procedure, and 4) display the centerline

result and export the centerline as clouds of points. You can

refer to the diagram shown in Fig. 2 for more details.

III. RESULTS

In this section, we are going to compare the centerline

extraction results of both methods on several sets of liver

vessel data. Since our segmented vessel data is in the vol-

umetric format, we will simply use the 3D slicer to build the

mesh model of it and use it as input to the VMTK. For the

skeletonize (2D/3D) plug-in in the ImageJ, we only need to

make sure that the binary image is in the 8 bit greyscale image

format.The first example is conducted on the portal vein volume

data from one set of our CT image datasets. The skeletonized

results from ImageJ are shown in Figs. 3 (a)-(b). As can be

seen from Fig. 3 (b), the skeleton representation of the port

vein is accurate, though a bit discontinuous. It is possible that

volume data is a bit noisy. A pre-processor such as 3-D digital

filter will be helpful in this case as suggested in [14]. It is also

of interest to know that the implementation of the centerline

extraction is rather fast. With a i7 920 PC, 2.66 Ghz CPU,

and 6G ram, the skeletonisation on a 512×512×191 volume

data only takes around 15 seconds. Furthermore, there is no

user interaction to choose the seeds or select some landmarks

on the vessels.The results from VMTK are shown in Figs. 4 (b)-(c). We

use the Editor module in 3D slicer to build the surface model

of the volumetric data and then use the VMTK to extract the

centerlines. Fig. 4 (a) shows the surface model of the portal

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(a)

(b) (c)

(d) (e)

Fig. 2. The process of centerline extraction using VMTK. (a) The 3D polydatamodel. (b) The surface model after closing. (c) Centerline output resultsimposed with Voronoi diagram and surface model. (d) Voronoi diagram. (e)Centerlines.

(a) (b)

Fig. 3. Example 1 - ImageJ. (a) The 3D volume data displayed in ImageJ. (b)The skeletonized result.

vein volume data we used in ImageJ. For VMTK, it requires

at least one starting seed and one target seed. In this example,

we select one starting seed (denoted as Seeds-P) and 14 target

seeds (denoted as Targets-P and followed by numbers). For the

sake of simplicity and stability, we didn’t place seeds at every

small branches. For one hand, it is tedious and difficult to place

seeds at the ends of all branches. On the other hand, placing

too many seeds will make the VMTK prone to errors and

unstable during the extraction process. Actually, the centerlines

of all branches have been extracted as shown in Fig. 4 (c),

while the computed centerlines are displayed based on the

seeds you chosen. It is possible to extract all the centerlines

and produce the extraction results which is similar to ImageJ’s.

Compared Fig.3 (b) and Fig. 4 (d), we can say that the

result from VMTK is better than the result from ImageJ, which

(a) (b)

(c) (d)

(e) (f)

Fig. 4. Example 1 - VMTK. (a) The 3D polydata model created by 3D Slicer.(b) The result after running the VMTK. (c) The Voronoi diagram. (d) Thecenterlines. (e) Zoomed picture of the VMTK result. (f) Zoomed picture ofthe obtained Voronoi diagram.

looks a bit irregular and discontinuous. Moveover, beside the

centerlines, the VMTK also outputs the maximal inscribed

sphere radius that are useful in reconstruction of better vessel

visualizations. However, the computational time for VMTK is

quite long, especially when the surface models are not smooth

and with lots of spurs. In this example, it takes around 20

minutes to get the centerline extraction results. Unlike the

plug-in in ImageJ, VMTK requires user interactions to choose

the seeds on the vessels.

Let us look at another example. Another portal vein vessels

tree is extracted and shown in Fig. 5 (a), and the skeletonized

result is provided in Fig. 5 (b). When looking at the skeleton,

the thinning algorithm seems to fail in several first order

branches. And the skeleton looks more blocky than the previ-

ous example, though these two CT image datasets are of the

same slices thickness. The VMTK provides a more consistent

result as shown in Figs. 6 (a)-(d).

IV. CONCLUSIONS

In this paper, we study the centerline extraction approaches

in two popular medical image analysis toolkits, i.e. ImageJ and

3D slicer. These two plug-ins or extensions, named Skeletonize

(2D/3D) and VMTK, are both effective in extracting the

centerlines of liver blood vessels. Skeletonize (2D/3D) has

the advantages of speed, automatic, and simplicity. It dose not

require surface model of the vessel trees, as well as the user

interaction of choosing the seed points. The extraction process

is very fast, which makes it very useful when one needs only

to quickly exam the skeleton representation of the vessels.

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(a) (b)

Fig. 5. Example 2 - ImageJ. (a) The 3D volume data displayed in ImageJ. (b)The skeletonized result.

(a) (b)

(c) (d)

Fig. 6. Example 2 - VMTK. (a) The result after running the VMTK. (b) TheVoronoi diagram. (c) The centerlines. (d) The centerlines superimposed onthe surface model.

According to this study, several improvements can be made.

For the skeletonize (2D/3D) method in ImageJ, it is feasible

to utilize the vessel connectedness information together with

the obtained skeleton results to construct more smooth and

continuous centerlines. For the VMTK method in 3D slicer,

a good morphological operation should be applied on the

3D surface model to smooth out noises before performing

the centerline extraction process. This will not only help to

improve the centerline extraction results, but also help to

reduce the computational load and increase the accuracy of

the vessel diameter information. For both methods, a vessel

tree ranking scheme could be intergraded, as this will be an

important follow-up application for centerline extraction. The

ranking results are also important in the fields of liver partition,

liver surgical planing and tumor analysis.

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