Computer-Aided Diagnosis of Liver Tumor in CT Image
Date Issued
2015
Date
2015
Author(s)
Yang, Ming-Yang
Abstract
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.
Subjects
computed tomography
liver
diagnosis
grey level co-occurrence matrix
elliptic model
kinetic curve
SDGs
Type
thesis
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