Qiao Xu, Chen Yen-Wei
College of Information Science and Engineering, Ritsumeikan University, 525-0054 Kusatsu, Japan.
Int J Biomed Imaging. 2011;2011:601672. doi: 10.1155/2011/601672. Epub 2011 Oct 16.
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.
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对肝脏形状进行归一化，以消除肝脏形状的变异性并捕捉纯纹理变化。当训练样本比数据维度少得多时，GND-PCA用于克服过拟合问题。留一法实验的初步结果表明，我们的方法构建的肝脏统计纹理模型能够很好地表示未训练的肝脏体积，即使模型是由较少的样本训练得到的。我们还展示了其在正常肝脏和异常（有肿瘤）肝脏分类中的潜在应用。
