以電腦斷層掃描影像做電腦輔助肝臟腫瘤診斷

Abstract

在美國，由於肝癌是近年來排名第十的常見癌症，因此對病患來說，及早的診斷和治療是非常重要的。在眾多醫學檢測儀器中，電腦斷層掃描是非常常見的一種方式用來檢查和掃描肝臟組織，經由注射顯影劑，電腦斷層掃描會在不同時間點掃描整顆肝臟並記錄。本篇論文的目標是以電腦斷層掃描的影像做肝臟腫瘤的良惡性診斷。本篇論文中有兩種不同的實驗資料，一種是四個時間點的電腦斷層掃描影像，另一個是三個時間點的電腦斷層掃描影像。這兩種實驗資料將會各別分開做完全相同的實驗。在這個電腦輔助診斷系統中，使用者標記出腫瘤後將採用類區域生長演算法切割。切割完畢後，三種特徵包括紋路、形狀和動力曲線特徵將會從腫瘤中擷取出來。紋路特徵是根據灰階值共生矩陣來定義三維空間中腫瘤的紋理特性。緊實度、邊緣變化和橢圓模型是形狀特徵中用來描述三維空間中的腫瘤形狀。最後一個動力曲線特徵則是擷取各個時間的腫瘤影像，並分析它們之間的亮度值變化。我們根據這三種特徵對兩種實驗資料做了兩組實驗。其中四個時間點的電腦斷層掃描影像實驗中，有40顆腫瘤包含29顆良性11顆惡性做為實驗資料，實驗結果達到準確性77.5% (31/40) 、敏感性72.73% (8/11) 、專一性79.31% (23/29)與AZ值0.7791。另外，在三個時間點的電腦斷層掃描影像實驗中，有71顆腫瘤包含49顆良性22顆惡性做為實驗資料，實驗結果達到準確性81.69% (58/71) 、敏感性81.82% (18/22) 、專一性81.63% (40/49)和AZ 值0.8713。由實驗結果可知，準確性、敏感性、專一性和AZ 值，三個時間點的電腦斷層掃描影像實驗的結果都勝於三個時間點的電腦斷層掃描影像實驗的結果。

Liver cancer is the tenth most common cancer in the recent years in USA. Therefore, early detection and well treatment are very important for the patient. Computed tomography (CT) is one of the most common and robust imaging techniques for the detection of liver. CT scanners allow multiple-phase sequential scans of the whole liver to be obtained during the injection of contrast material. In this paper, the main purpose is to build a computer-aided diagnosis (CAD) system to extracted features from tumors and diagnose the liver tumor in multiple-phase CT. There are two kinds of data in this paper, one is the four phase CT images and the other is three phase CT images. The experiment of two kinds of data will do in the same way but separately. In the proposed CT computer-aided diagnosis (CAD) system, the tumor was indicated by user and the tumor was segmented by a region growing algorithm. After tumor segmentation, three kinds of features were extracted from the tumor including texture features, shape features, and kinetic curve features. The texture features quantify 3 dimensions (3-D) texture information of tumor based on the grey level co-occurrence matrix. Compactness, margin and elliptic model were used to describe the 3-D shape information of tumor in the shape features. The last kind of features is the kinetic curve features which was extracted from each phase of tumor and represent the intensity variation between each phase. By analyzing the three kinds of features in the three phase and four phase CT images, we have two experiment results. In the experiment of four phase CT images, 40 tumors with 29 benign and 11 malignant tumors were used in this CAD system to evaluate the performance. The accuracy, sensitivity, specificity, and AZ were up to 77.5% (31/40), 72.73% (8/11), 79.31% (23/29), and 0.7791, respectively. In the experiment of three phase CT images, 71 tumors with 49 benign and 22 malignant tumors were used in this CAD system to evaluate the performance. The accuracy, sensitivity, specificity, and AZ are up to 81.69% (58/71), 81.82% (18/22), 81.63% (40/49), and 0.8713. As a result, the accuracy, sensitivity, specificity, and AZ are better in the experiment of three phase CT images than four phase CT images.