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首页> 外文会议>2010 4th International Conference on Bioinformatics and Biomedical Engineering >Study on Feature Extraction for Ultrasonic Differentiation of Liver Space-Occupying Lesions
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Study on Feature Extraction for Ultrasonic Differentiation of Liver Space-Occupying Lesions

机译：肝脏占位性病变超声鉴别特征提取的研究

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this study proposes a set of novel feature vectors for accurate differentiation of 3 typical types of liver space-occupying lesions in ultrasound images. Experiments were performed on 280 cases of liver images, including 112 cases of normal liver images, 90 cases of liver cancer images, 38 cases of liver hemangioma images and 40 cases of liver cyst images. First, we defined two types of region of interest and extracted a series of new features according to general image analysis and clinical diagnosis criteria. Second, the extracted features were roughly screened by U test and correlation analysis. The backward-removal feature sequences were obtained by quadratic mutual information. Third, the suboptimum feature vectors were determined as input to the three-level back-propagation artificial neural network (BP ANN). Finally, the proposed BP ANN was evaluated on total 280 cases by means of 'leave-one-out' methods. The precise differentiation rate of liver cancer, liver hemangioma, liver cyst and normal liver are 100%, 94.7%, 95% and 100%, respectively. The results indicate that the new defined features are useful to achieve high accurate differentiation of liver space-occupying lesions.
机译：这项研究提出了一套新颖的特征向量，用于在超声图像中准确区分3种典型类型的肝脏占位性病变。对280例肝脏图像进行了实验，包括112例正常肝脏图像，90例肝癌图像，38例肝血管瘤图像和40例肝囊肿图像。首先，我们定义了两种类型的感兴趣区域，并根据常规图像分析和临床诊断标准提取了一系列新特征。其次，通过U检验和相关分析粗略筛选提取的特征。通过二次互信息获得后向去除特征序列。第三，将次优特征向量确定为三级反向传播人工神经网络（BP ANN）的输入。最后，通过“留一法”对280例拟议的BP神经网络进行了评估。肝癌，肝血管瘤，肝囊肿和正常肝的精确分化率分别为100％，94.7％，95％和100％。结果表明，新定义的特征可用于实现肝脏占位性病变的高精度区分。

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