This paper proposes a set of rotation invariant features based on three dimensional Gaussian Markov Random Fields (3D-GMRF) for volumetric texture image classification. In the method proposed here, the mathematical notion of spherical harmonics is employed to produce a set of features which are used to construct the rotation invariant descriptor. Our proposed method is evaluated and compared with other method in the literature for datasets containing synthetic textures as well as medical images. The results of our experiments demonstrate excellent classification performance for our proposed method compared with state-of-the-art methods. Furthermore, our method is evaluated using a clinical dataset and show good performance in discriminating between healthy individuals and COPD patients. Our method also performs well in classifying lung nodules in the LIDC-IDRI dataset. Our results indicate that our 3D-GMRF-based method enjoys more superior performance compared with other methods in the literature.

本文提出了一种基于三维高斯马尔可夫随机场（3D-GMRF）的旋转不变特征集，用于体积纹理图像分类。在这里提出的方法中，采用球谐函数的数学概念来产生一组特征，这些特征用于构造旋转不变描述符。对于包含合成纹理以及医学图像的数据集，我们对本文提出的方法进行了评估，并与文献中的其他方法进行了比较。实验结果表明，与最新方法相比，我们提出的方法具有出色的分类性能。此外，我们的方法是使用临床数据集进行评估的，在区分健康个体和COPD患者方面显示出良好的性能。我们的方法在LIDC-IDRI数据集中的肺结节分类中也表现出色。我们的结果表明，与文献中的其他方法相比，基于3D-GMRF的方法具有更好的性能。