基于高阶导数变换的纹理特征在膀胱肿瘤中的应用研究

Texture features based on high-order derivative maps for differentiation of bladder cancer

目的 :验证从MRI灰度图像及其高阶偏导图像中提取的三维纹理特征,表征出膀胱肿瘤和壁组织之间的纹理差异,从而实现对膀胱肿瘤与壁组织的鉴别。方法:从62名临床确诊的膀胱癌患者的T2加权MRI数据中提取出感兴趣区容积(volume of interest,VOI)的62例癌和62壁体积。从每个VOI中计算出三维高阶偏导映射(梯度和曲度图),然后从每个VOI提取基于灰度及其高阶偏导图的三维Haralick特征及基于灰度图的Tamura特征,以更好地反映肿瘤组织的异质性分布。结果:每个VOI共提取出58个纹理特征,其中37个特征显示出显著的组内差异(P≤0.01)。结论:从灰度及其高阶偏导图像提取的三维纹理特征可以更好地反映癌组织的异质分布。

Objective To determine the three-dimensional（3D） texture features extracted from intensity and high-order derivative maps that could reflect textural differences between bladder tumors and wall tissues, in order to achieve bladder cancer and wall tissue identification. Methods A total of 62 cancerous and 62 wall volumes of interest（VOI） were extracted from T2-weighted MRI datasets of 62 patients with pathologically confirmed bladder cancer. To reflect heterogeneous distribution of tumor tissues, 3D high-order derivative maps（the gradient and curvature maps） were calculated from each VOI. Then 3D Haralick features based on intensity and high-order derivative maps and Tamura features based on intensity maps were extracted from each VOI. Statistical analysis was proposed to first select the features with significant differences and then obtain a more predictive and compact feature subset to verify its differentiation performance. Results From each VOI, a total of 58 texture features were derived. Among them, 37 features showed significant inter-class differences（P ≤0.01）. Conclusion The results suggest that 3D texture features deriving from intensity and high-order derivative maps can reflect heterogeneous distribution of cancerous tissues.