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CT CT *2 EM 3 CT x 26 24 Key words : liver, EM-algorithm, dynamic CT, segmentation, computer aided diagnosis 1. CT CT [ CT
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Hepatocellular carcinoma:  HCC
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2 2.3.1 EM EM (1) CT [Studholme99] 4
2(b) CT
(1) Q
2.3 CT CT CT 2 CT
CT CT 2 CT CT 3 d EM
(1)デュC (c)
X n Ψ d CT Fig. 3 Examples of CT slices and CT value histograms in early and late arterial phases. 2.3.2 3(a) CT CT
2.3.3 CT CT
CT CT
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EM
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Ψ (0)
2.4 2 Ψ i Ψ mデュ2.3 Ψ i Ψι 2
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lmin lmin < Tmin ∧ (0 < v2 < μι 2 = 0.5 σι 22 ) (14)
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1 2 2 r L' C 6 CT CT Fig. 5 Liver region obtained by combining initial liver, HCC, and other organ regions. Ψ l
Ψ m Closing o 2.4.4 2.4.1
5 L'
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4
5
Ψ m 2 5 L' CT Table  1 Coincidence rates of the extraction results against manually traced regions.

Table 2  Evaluation results of extracted liver
regions by visual inspection. The result contains whole liver region and HCC. The result contains HCC region and the liver region has some under or over extraction. × The result does not have HCC region or the liver region has large under or over extraction.
26
CT
: :
1.0 mm
5 mm
6 (a) Initial liver region L (b) HCC region H Extracted initial liver region L and Fig. 7 HCC region H of Case 13. Fig. 8  The result of HCC region H extracted from Case 19.  (a) HCC regons H are extracted by using thresholding and morphology opera- tion.  (b) HCC regionss H are eliminated by evaluating distances from liver region L' and circularities.
CT 1 2 20 mm × 4.
T min = 3mm  T c = 0.85 OS: WindowsXP CPU: Xeon 3.4GHz×2 4 3. .. (c) Estimated distribution Ψ ι Fig. 9 Estimated distributions Ψ 1 , Ψ m , and Ψ s from the joint histogram of Case 1. 90 %
(13)
Case 18 6(h)
CT CT EM
(14)
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4 8(a) L' 8(b) 26
[ (j) CT 2 CT 4 1 9(b) 2 2 2.3.2 Case 1 2.3.3 21 CT EM EM CT 10 H.U. ψι² ψε ψπ ψι 0.5 H.U. CT ±10 H.U. fa μ  0.5 H.U. 2.3.2 2.3.3 99 % CT CT 21 COE hrc CT CT CT ( ( ( CADM 00] 00] News Letter  Vol. 28  pp. 12–13   Jan 2000 01] CADM 03] 03] News Letter   Vol. 38   pp. 6–7   2003 05] ,CADM News Letter  Vol. 43  pp. 4–6   2005 CADM : CT 03] xとX  2 (21) CT 5. CT 2 EM 2 3 X 26 24 CT 4 CT 2.5 ( ( CT CT CT
[ 03]
CT Vol.7 No.4-3 pp. 59–66 2003 : X CT X  CT Vol.8 No.1-3 pp.10–17 2004 [ 04] : 3 Vol.8 No.1-3 pp. 18–30 2004 04] [ ) 2002 [ 04] CT [ 13 295–296 2004 01] No. 9   pp. 2150–2161   2001 [ 03] CT . Vol. 7   No. 4-1   pp. 1–10   2003 : Watershed 14 CT : D-II  Vol. J84–D-II pp- 2003 3 [ 04] : Vol. 8  No. 1-1  pp. 1–9   Nov 2004. [Dempster77] A. P. Dempster, N. M. Laird, and D. B. Rubin : Maximum Likelihood from Incomplete Data via the EM Al- gorithm, Jornal of the Royal Statistical Society, Series B, Vol. 39, No. 1, pp. 1–38, 1977. [Rueckert99] D. Rueckert, L. I. Sonoda, C. Hayes, et al. : Non- rigid Registration Using Free-Form Deformations Application to Breast MR Images, IEEE Transactions on Medical Imag- ing, Vol. 18, No. 8, pp. 712–721, 1999 [Studholme99]  C. Studholme, D. L. G. Hill, and D. J. Hawkes :  An overlap invariant entropy measure of 3D medical alignment, Pattern Recognition, Vol. 32, pp. 71–86, 1999 [Maurer03] C. R. Maurer, Jr., R. Qi, and V. Raghavan : A Lin- ear Time Algorithm for Computing Exact Euclidean Distance Transforms of Binary Images in Arbitrary Dimensions, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 25, No. 2, pp. 265–270, 2003 "  http://www.tuat.ac.jp/ simi- [CADM] “3 ) IEEE ) 1967 2003 2004 3 NTT 1997      2 2003     4 3 21 COE COE ( ) 1991 1996 1998 1996 2000         16 NICOGRAPH/MULTIMEDIA 2004 MIRU2004 II (        )   3 (      ) ） ( IEEE I
Yuichiro Hayashi*¹ Takayuki Kitasaka*¹ Daisuke Deguchi*¹ Yasuhito Suenaga*¹ Jun-ichi Hasegawa*² Kensaku Mori*¹ Yoshito Mekada*² Junichiro Toriwaki*² *¹ Graduate School of Information Science, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Aichi, 464–8603 Japan *² School of Life System Science and Technology, Chukyo University, 101 Tokodachi, Kaizu-cho, Toyota, Aichi, 470–0393, Japan
Abstract
This paper proposes a method for automated liver region extraction by analyzing distributions of CT values from multi-phase 3D abdominal X-ray CT images. A simple thresholding method does not work well for extracting liver regions from single phase CT images, since CT values of a liver region are very similar to those of other organs such as the spleen and muscle. 'The proposed method estimates CT value distributions of liver, spleen, and muscle regions using early and late arterial phase CT images. We assume these distributions as Gaussian and their mixture, and estimate the distributions by the EM algorithm. The estimated distributions are used for determining the optimum threshold values for extracting initial liver region, Hepatocellular carcinoma (HCC), and other organs. Then, the liver region is obtained by combining initial liver region and HCC region, and other regions are used for eliminating over extraction of the liver region. Finally, the surface of the extracted liver region is smoothed along the border of the liver region. We applied the proposed method to 26 cases of multi-phase abdominal CT images. The experimental results showed that the proposed method succeeded in the satisfactory extraction of liver regions.