COMPUTER-AIDED DIAGNOSIS FOR THE CLASSIFICATION OF BREAST MASSES IN AUTOMATED WHOLE BREAST ULTRASOUND IMAGES
COMPUTER-AIDED DIAGNOSIS FOR THE CLASSIFICATION OF BREAST MASSES IN AUTOMATED WHOLE BREAST ULTRASOUND IMAGES
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DOI：
10.1016/j.ultrasmedbio.2011.01.006
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发表时间：
2011-04-01
影响因子：
2.9
通讯作者：
Chang, Ruey-Feng
中科院分区：
文献类型：
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作者：
Moon, Woo Kyung;Shen, Yi-Wei;Chang, Ruey-Feng
最近已经开发了新的自动全乳房超声（ABUS）机器，并且可以以标准方式采集全乳房的超声（US）体积数据集。本研究的目的是开发一种新的计算机辅助诊断系统，用于ABUS图像中乳腺肿块的分类。147例（76例良性和71例恶性乳腺肿块）是通过商业可用的ABUS系统获得的。由于ABUS图像中相邻切片的距离是固定的且很小，因此这些连续切片被用于重建为三维（3-D）US图像。采用水平集分割方法对三维肿瘤轮廓进行分割。然后，在分割的三维肿瘤轮廓的基础上，提取肿瘤的纹理、形状和椭球拟合等三维特征，基于Logistic回归模型对肿瘤进行良恶性分类。采用Student t检验、Mann-Whitney U检验和受试者工作特征（ROC）曲线进行统计学分析。从ROC曲线的Az值来看，形状特征（0.9138）优于纹理特征（0.8603）和椭球拟合特征（0.8496）。形状和椭球体拟合特征之间的差异显著（p = 0.0382）。然而，椭球拟合特征和形状特征的组合可以实现最佳性能，准确性为85.0%（125/147），灵敏度为84.5%（60/71），特异性为85.5%（65/76），ROC曲线下面积Az为0.9466。结果表明，ABUS图像可用于乳腺肿瘤的计算机辅助特征提取和分类。（电子邮件：rfchang@csie.ntu.edu.tw）（C）2011年世界医学和生物学超声联合会。
New automated whole breast ultrasound (ABUS) machines have recently been developed and the ultrasound (US) volume dataset of the whole breast can be acquired in a standard manner. The purpose of this study was to develop a novel computer-aided diagnosis system for classification of breast masses in ABUS images. One hundred forty-seven cases (76 benign and 71 malignant breast masses) were obtained by a commercially available ABUS system. Because the distance of neighboring slices in ABUS images is fixed and small, these continuous slices were used for reconstruction as three-dimensional (3-D) US images. The 3-D tumor contour was segmented using the level-set segmentation method. Then, the 3-D features, including the texture, shape and ellipsoid fitting were extracted based on the segmented 3-D tumor contour to classify benign and malignant tumors based on the logistic regression model. The Student's t test, Mann-Whitney U test and receiver operating characteristic (ROC) curve analysis were used for statistical analysis. From the Az values of ROC curves, the shape features (0.9138) are better than the texture features (0.8603) and the ellipsoid fitting features (0.8496) for classification. The difference was significant between shape and ellipsoid fitting features (p = 0.0382). However, combination of ellipsoid fitting features and shape features can achieve a best performance with accuracy of 85.0% (125/147), sensitivity of 84.5% (60/71), specificity of 85.5% (65/76) and the area under the ROC curve Az of 0.9466. The results showed that ABUS images could be used for computer-aided feature extraction and classification of breast tumors. (E-mail: rfchang@csie.ntu.edu.tw) (C) 2011 World Federation for Ultrasound in Medicine & Biology.