計測自動制御学会東北支部 第 295 回研究集会 (2015.6.26)
資料番号 295-3

局所厚み特徴に基づくグラフカット領域分割を用いた
高精度肝臓抽出Accurate Liver Extraction Using a Local-Thickness-Based
Graph-Cut Approach○張山 昌論 ∗ ，下田 貢 ∗∗ 小林 康浩 ∗∗∗○ Masanori Hariyama∗ , Mitsugi Shimoda∗∗
Yasuhiro Kobayashi∗∗∗*東北大学, **獨協医科大学, ***小山高専*Tohoku University, **Dokkyo Medical University,** National Institute of Technology, Oyama College
キーワード : 医療画像処理 (Medical imaging), 3 次元シミュレーション (3D Simulation),
解剖学的肝切除 (anatomical hepatectomy), 局所厚み特徴 (local thickness)

連絡先 : 〒 980-8579 仙台市青葉区荒巻字青葉 6-6-05 東北大学大学院情報科学研究科 張山昌論，Tel.: (022)795-7153，Fax.: (022)263-9167，E-mail: hariyama@ecei.tohoku.ac.jp

1.

Introduction

liver shapes. The active contour approach 3)

For liver cancer surgery, 3D simulation before surgery operations, recently, is getting oneof the crucial tasks since a liver has complex
structure. Liver segmentation from CT images
is a challenging task since the variations of theliver shape is large and since there exist someother anatomical structures with the CT values similar to the liver around the liver. For
example, Fig. 1(a) shows a CT image. Ribmuscle contacts Liver partly, and they have almost similar CT values. There have been several researches on liver extraction1) -5) . Theresearches 1) and 2) use, respectively, statistical shape models and probabistic atlases, andare dependent on image gradient, and leads
to over-extraction into organs with CT values similar to the liver. Moreover, its quality
strongly relies on the location and shape of theinitial contour. The intensity-based approach4) usually exploits a simple intensity model,and miss the vessels and non-homogenous texture regions inside the liver. The structuremodel-based approach5) exploits a shape andvessel models as well as an intensity one. Although it successfully extracts the major volume of liver, there is still over-extraction atregions touching the liver tightly like rib muscle as shown in Fig. 1.

both methods suﬀers from large variations of–1–

Fig. 1CT image including the liver and extraction result of conventional approach5) .

Fig. 2

Fig. 3

Flow of extracting the liver regions.

Edge images: binary image and local-thickness image.

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2.

Algorithm

cut segmentation works in such a way that re-Figure 2 shows the total flow of extracting aliver region. For simplicity, we use 2-D images

gions with small LT values are segmented intobackground.

in the figure although a 3-D image is used infact. First, a liver candidate region is extracted
from CT images using structure-model-based
method5) . The upper-left and upper-right figures shows a CT image and the liver candidate region.

Readers can see rib muscle ismis-extracted as the liver region. Next, forthe liver candidate region, the thickness feature is measured by computing “Local Thickness“ 6) , where the local thickness of a point isdefined as the diameter of the largest spherethat fits inside the object and contains thepoint. The lower-right figure of Fig. 2 shows
the local-thickness image of the liver candidate
image. Generally speaking, the core region of
the liver has a large LT value whereas misextracted regions surrounding the liver tends

3.

Experimental results

For programming, we use the Java-based image processing platform called ImageJ10) . ImageJ has a lot of plugins like a DICOM reader,
basic 2D/3D image processing and visualization, and can be extended easily by adding
use-defined plugins. Moreover, ImageJ runs on
multi-platforms like Microsoft Windows, Linuxand MacOS since it is based on Java. To bespecific, we use Fiji, one distribution of ImageJ, since it has much more plugins such asgraph cut segmentation than original ImageJ.
For the experiments, we use the plugin of graphcut segmentation which is implemented basedon 8) . This plugin has for parameters to bepreset as follows:

to have a small LT value. This observation• the expected numbers of foreground pix-

indicate that the local thickness image is aels,

good metrics to give the possibility which voxel
is foreground(or background) one. Figure 3• the smoothness of the segmentation,shows the diﬀerence between the binary andLT images of the liver candidate region. In the• the influence of the edge image,binary image, the mis-extracted region like rib• the variance of the edge image.

muscle has the same weight value as correctlyextracted region. In the LT image, the mis-

We adjust these parameters as needed.

extracted region has smaller weight value thanLet us compare the proposed method(graph-the correctly-extracted region. Finally, graphcut segmentation using LT edge images) withsegmentation based on structure models5) , andcut segmentation is done using the LT image
as the edge image as shown in the lower-left
figure. The graph-cut segmentation 7) -9) is awith graph-cut segmentation using binary edgeenergy-based approach which allow much morestructure model is used to obtain the liver can-robust segmentation than simple techniques such

didate region in our extraction flow shown inas region growing or split-and-merge. The CT

Fig. 2. The comparison results are shown inimage and the LT image are used as inputs

Figures 4-6; the regions bounded with dotted

for the graph-cut segmentation. The graph-lines indicate the mis-extracted regions; note

images. Note that the segmentation based on–3–

Fig. 4Comparison 1.

Fig. 6Comparison 3.

Fig. 5–4–Comparison 2.

that there are only over-extracted regions in3) R.S. Alomari, S. Kompalli, and V. Chaud-this experiments. Each of these figures have

hary,(a) Grand truth, (b) Extraction based on struc-dominal CT using Markov random field

ture model, (c) Graph-cut segmentation using

model and GVF snakes,

binary edge images, and (d) Graph-cut seg-ternational Conference on Complex, In-

mentation using LT images(proposed). In thesetelligent and Software Intensive Systems,results, there are over-extracted areas in (b),pp.293-298(2008).

and they are improved in the proposed method(d). In contrast, the results of (c) are almost
same as those of (b) in Figs. 5 and 6, and iseven worse in Figure. 4. The another advantage of the proposed method is that it is lesssensitive than the Graph-cut segmentation us-Segmentation of the liver from abProc. 2008 In-4) A.H. Foruzan, R.A. Zoroofi, M. Hori, andY. Sato,

A knowledge-based techniquefor liver segmentation in CT data, Computerized Medical Imaging and Graphics,
vol.33, no.8, pp.567-587(2009).
5) Masanori Hariyama, Riichi Tanizawa,ing binary edge image.

Mitsugi Shimoda, Keiichi Kubota, Ya-4.

suhiro Kobayashi, ”Liver Extraction fromConclusion

CT Images Based on Liver Structure

The proposed method can obtain the accu-Models”, Proc.International Conference

rate boundary of liver region based on the in-on Image Processing, Computer Vision,formation of local thickness. As future work,and Pattern Recognition(IPCV), pp.170-

setting parameters automatically is important

173, (2014).

issue. Moreover, simultaneous recognition ofother organs around the livers is on-going to6) R.

Dougherty“Computingimprove the accuracy.

andlocalK.

Kunzelmann,

thicknessstructures with ImageJ“,croscopy&Microanalysisof3D

Proc. MiMeeting,

参考文献

www.optinav.com/LocalThicknessEd.pdf

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