Mayerhoefer Marius E, Breitenseher Martin J, Kramer Josef, Aigner Nicolas, Hofmann Siegfried, Materka Andrzej
Osteoradiology Section, Department of Radiology, Medical University of Vienna, Austria.
J Magn Reson Imaging. 2005 Nov;22(5):674-80. doi: 10.1002/jmri.20429.
To investigate the reproducibility and transferability of texture features between MR centers, and to compare two feature selection methods and two classifiers.
Coronal T1-weighted MR images of the knees of 63 patients, divided into three groups, were included in the study. MR images were obtained at three different MR centers. Regions of interest (ROIs) were drawn in the bone marrow and fat tissue. Then texture analysis (TA) of the ROIs was performed, and the most discriminant features were identified using Fisher coefficients and POE+ACC (probability of classification error and average correlation coefficients). Based on these features, artificial neural network (ANN) and k-nearest-neighbor (k-NN) classifiers were used for tissue discrimination.
Although the texture features differed among the MR centers, features from one center could be successfully used for tissue discrimination in texture data on MR images from other centers. The best results were achieved using the ANN classifier in combination with features selected by POE+ACC.
The differences in texture features extracted from MR images from different centers seem to have only a small impact on the results of tissue discrimination.
研究磁共振成像（MR）中心之间纹理特征的可重复性和可转移性，并比较两种特征选择方法和两种分类器。
本研究纳入63例患者膝关节的冠状位T1加权MR图像，这些患者被分为三组。MR图像在三个不同的MR中心获取。在骨髓和脂肪组织中绘制感兴趣区域（ROI）。然后对ROI进行纹理分析（TA），并使用Fisher系数和POE+ACC（分类错误概率和平均相关系数）确定最具判别力的特征。基于这些特征，使用人工神经网络（ANN）和k近邻（k-NN）分类器进行组织鉴别。
尽管不同MR中心的纹理特征有所不同，但一个中心的特征可成功用于其他中心MR图像纹理数据的组织鉴别。使用ANN分类器结合POE+ACC选择的特征可获得最佳结果。
从不同中心的MR图像中提取的纹理特征差异似乎对组织鉴别结果影响较小。
