IPSJ SIG Technical Report Vol.2013-CVIM-187 No /5/30 CT CT CT CT CT,,, Research on Automatic Liver Region Detection from Multi-Slice

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1 CT CT CT CT CT,,, Research on Automatic Liver Region Detection from Multi-Slice Abdominal CT Images Abstract: In the diagnosis of liver affection of cirrhosis and hepatocellular carcinoma,etc, the CT Images are generally used. However, the physician must scrutinize the many CT Images, therefore, the problem is the heavy burden on the physician. Then, we have developed a method of auto-extracting the whole liver from an abdomen CT Images due to aided diagnosis through quantitative as second opinion. First, analyze the distribution of brightness of the original image. Second, determine the threshold of binarization automatically, and do it. Next, it reduces the noise using morphology operation and labeling processing. And, determine the boundaries of the crude extraction region by applying a active contour model. Finally, we extract the whole liver region precisely using graph cut method which automated the parameter learning by adaptively choosing the pixels of the inside of boundary. Experimental results of clinical abdominal CT Images have shown the fundamental effectiveness of the proposed method. Keywords: Abdomen multi-slice CT Image, Liver segmentation, Graph Cut Method, Computer-aided diagnosis Graduate School of Science and Engineering, Kagoshima University CT CT CT c 2013 Information Processing Society of Japan 1

2 CT 2. CT 90% [1][2][3] Lim [1] CT ROI [2] Deng Wang Li [3] CT [1][2][3], [4] [5] 88.6% 89.3% [6] [7] 90% CT 粗抽出 精密抽出 前処理 明度分布解析 二値化 ノイズの低減 輪郭検出 繰返し判定 閾値の伝搬 パラメータの設定 1 グラフカット 領域の補完 スライスの指定 平滑化 明度分布解析 平均と標準偏差の導出 2 二値化 膨張 収縮 ラベリング 輪郭検出 繰返し判定 閾値の伝搬 c 2013 Information Processing Society of Japan 2

3 CT CT 1 n 1 2 s n=1.25 s= Kass (Snakes[8]) Snakes 指定スライスとその上下 2 スライスの閾値調査 5 スライスの閾値の平均導出 閾値を他のスライスへ適用 3 精密抽出 全領域を背景と設定 矩形の描画 格子線の描画 格子線の位置判定 内分点の描画 内分点の位置判定 内分点の明度判定 直線を描画し前景と設定 矩形の終端かどうか グラフカット 輪郭検出 CT m m+1 m= Boykov [9] I A = {A x } x I A x ( ) ( ) L = 0, 1 a x c 2013 Information Processing Society of Japan 3

4 a E(a D) = log(a = a D) D () ( ) E(A D) = ψ1 (D A x ) + ξ 1 (A x ) x a{ + } (ψ 2 (D A x, A y ) + ξ 2 (A x, A y )) (1) y N x C x x ψ i (D A x ) = log p(c x A x ) p(a x ) ψ 2 (D A x, A y ). (1) A x v x v x ( ) x N x neighborhood link (n-link) v x v y n-link(x y) c(v x, v y ) c(s, v x ) = ψ 1 (D A x = 0) + ξ 1 (A x = 0) (2) (source s sink t) terminal link(t-link) source sink t-link c(s, v x ) c(v x, t) c(v x, t) = ψ 1 (D A x = 1) + ξ 1 (A x = 1) (3) c(v x, v y ) = ψ 1 (D A x, A y ) + ξ 2 (A x, A y ) (4) source sink [10] source sink Boykov Ford-Fulkerson [11] source sink source sink â Snakes c 2013 Information Processing Society of Japan 4

5 情報処理学会研究報告 図 6 パラメータの設定 (d) 症例 3 その 2 図 8 原画像 図 7 見落としの例 4. 評価実験 (d) 症例 3 その 2 図 9 真値 4.1 実験データ 実験データとして 第二著者から提供された腹部 CT 臨 床画像を用いた X 線 CT 装置からの臨床データは医用画 像機器の通信プロトコル及び画像フォーマットの標準規格 である DICOM(Digital Imaging and COmmunication in Medicine) 形式で出力される この DICOM データを変換 ソフトを用いて 16bit グレースケールに変換して用いた 4.2 実験対象 肝臓内に腫瘍や腹水を含む 腹部 CT5 症例 100 スライ スを処理の対象とした 実際に 1 回の撮影で得られる画像 はこれより多いが 肝臓を含むスライスのみを対象とした ので 1 症例当たり約 20 スライス程度である また 評価 の際に用いる肝臓領域の真値は 医学的知識のある第二著 (d) 症例 3 その 2 図 10 粗抽出結果 者の監修のもとに作成したものを使用した 処理に用いた 腹部 CT の一例を図 8 に その真値を図 9 に示す 4.4 閾値の伝搬 3.3 の処理結果を図 11 に 真値を図 11(a) に示す 図 4.3 肝臓領域の粗抽出結果 図 10 に肝臓領域の粗抽出を行った結果を示す 明るく 着色されている部分が抽出した部分である c 2013 Information Processing Society of Japan 11(b) は統計的推定に基づき閾値を設定し処理を 4 回繰り 返した結果 図 11(c) は閾値を伝搬させ処理を行った結果 である 5

6 情報処理学会研究報告 表 1 (a) 真値 (b) 輪郭検出結果 図 11 図 12 (c) 閾値を伝搬させた 結果 閾値の伝搬結果 (d) 症例 3 その 2 症例 1 のスライス毎の検出率 (%) と過剰検出率 (%) 検出率 過剰検出率 スライス スライス スライス スライス スライス スライス スライス スライス スライス スライス スライス スライス スライス スライス スライス スライス スライス スライス スライス スライス 平均 出率はそれぞれ以下の式で定義した 前景のパラメータ設定結果 検出率 = Sc 100(%) St 過剰検出率 = Sd St 100(%) St 表 1 から表 5 に症例 1 から症例 5 のスライスごとの検出 率と過剰検出率の結果を 表 6 に精密抽出の症例毎の平均 検出率 平均過剰検出率の結果を示す 表 1 から表 5 の全 ての症例で スライス 5 を基準とするスライスに指定した 5. 考察 5 症例 100 スライスの腹部 CT に対し肝臓領域抽出処理 (d) 症例 4 その 2 図 13 を行った 本実験結果に対して以下の考察を述べる 精密抽出結果 5.1 肝臓領域抽出 4.5 肝臓の精密抽出結果 図 1 の手法を用いることにより 肝臓領域の平均検出率 図 12 にグラフカットの前景のパラメータを自動学習さ は 94.9%であった 図 10(a), (c) のように 粗抽出の段階 せた結果を 図 13 に肝臓領域の精密抽出を行った結果を で精度の高い肝臓領域抽出ができた事例もあった しか 示す し 大半のスライスでは 10(b) のようにに肝臓領域の一部 の抽出に失敗することや 図 10(d) のように 肝臓領域以 4.6 実験結果 外を過剰に検出してしまうことが多かった 4.2 で示したデータに対して処理を行い 精密抽出の結 一方 粗抽出の結果をもとに行った精密抽出では図 13(d) 果をピクセル数により評価した 各面積は抽出結果の画素 の結果では 血管付近の肝臓領域でない部分を切り落とす 数を計算することによって求めている 肝臓領域の真値の ことに成功している また 図 13(e) の結果では 肝臓領 ピクセル数を St 検出面積のピクセル数を Sd 肝臓領域 域の端の部分の細かい抽出ができていることが分かる こ の真値と検出面積の一致面積を Sc とし 検出率と過剰検 のように 高い精度を達成できた理由として 図 12 で示 c 2013 Information Processing Society of Japan 6

7 2 2 (%) (%) (%) (%) (%) (%) (%) (%) Level Set c 2013 Information Processing Society of Japan 7

8 6 (%) (%) % Snakes LevelSet CT CT Level Set 3 6. CT [1] Seong-Jae Lim, Yong-Yeon Jeong, Yo-Sung Ho, Automatic liver segmentation for volume measurement in CT Image, Hoa Journal of Visual Communication and Image Representation, Vol.17, Issue 4, pp (2006) [2],,,,,,, X CT Vol.J91-D.7 (2008) [3] Deng Wang Li, Hong Jun Wang, Da Chen, Yong Yin Automated Liver Segmentation for Cone Beam CT Dataset by Probabilistic Atlas Construction Applied Mechanics and Materials Vols pp (2012) [4]. MI, Vol pp 79-84, (2009) [5] 3 CT ( ). MI, Vol , pp , (2011) [6] CT CT MI Vol 9.4 pp (2005) [7] Maklad Ahmed CT (CT). MI, Vol pp , (2010) [8] Michael Kass, Andrew Witkin, Demetri Terzopoulos, Snakes Active Contour Models, International Journal of Computer Vision, Vol.1, Issue 4, pp ,(1988) [9] Y.Boykov and G.F. Lea Graph cuts and efficient N- D image segmentation IJCV Vol 70 2 pp (2006) [10] P Elias A Feinstein and C E Shannon note on maximum flow through a network IRE Transactions on Information Theory IT-2 pp (1956) [11] L R Ford and D R Fulkerson Maximal Flow Through A Network Canadian Journal of Mathematics 8:pp (1956) [12],,,,,,. PRMU, Vol , pp 89-94, (2009) c 2013 Information Processing Society of Japan 8

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