A Statistical Texture Model of the Liver Based on Generalized N-Dimensional Principal Component Analysis (GND-PCA) and 3D Shape Normalization.
A Statistical Texture Model of the Liver Based on Generalized N-Dimensional Principal Component Analysis (GND-PCA) and 3D Shape Normalization.
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DOI：
10.1155/2011/601672
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发表时间：
2011
影响因子：
7.6
通讯作者：
Chen YW
中科院分区：
文献类型：
--
作者：
Qiao X;Chen YW
我们提出了一种基于广义n维主成分分析（GND-PCA）和三维形状归一化技术的肝脏统计纹理建模方法。三维形状归一化技术用于肝脏形状的归一化，以消除肝脏形状的可变性和捕获纯粹的纹理变化。GND-PCA用于克服训练样本比数据的维数少得多时的过拟合问题。初步的“留一”实验结果表明，该方法建立的肝脏统计纹理模型可以很好地表示未经训练的肝脏体积，尽管该模型使用较少的样本进行训练。我们还展示了它在正常和异常（有肿瘤）肝脏分类中的潜在应用。
We present a method based on generalized N-dimensional principal component analysis (GND-PCA) and a 3D shape normalization technique for statistical texture modeling of the liver. The 3D shape normalization technique is used for normalizing liver shapes in order to remove the liver shape variability and capture pure texture variations. The GND-PCA is used to overcome overfitting problems when the training samples are too much fewer than the dimension of the data. The preliminary results of leave-one-out experiments show that the statistical texture model of the liver built by our method can represent an untrained liver volume well, even though the mode is trained by fewer samples. We also demonstrate its potential application to classification of normal and abnormal (with tumors) livers.