An adaptive method based on three dimensional Gaussian Markov Random fields (3D-GMRF) is proposed in this paper for volumetric texture segmentation. A feature vector is extracted for each voxel in a given volumetric texture image using an estimation cube. However, the selection of the size for this estimation cube causes some fundamental issues related to the uncertainty principle and the inability of the model to capture different texture patterns. These issues are tackled here by employing an adaptive method where the size of the estimation cube is adaptively varying to capture different patterns and also minimize the number of voxels that are related to different texture classes inside the estimation cube. The feature vectors that consist of the estimated parameters of the GMRF and form the parameter volume are hence employed to segment volumetric textures. These features are smoothed by applying an averaging filter using an adaptive averaging technique. Such an averaging filter improves the segmentation results considerably. Our method proposed here is evaluated on a synthetic volumetric texture dataset and compared with other methods to demonstrate the superiority of our segmentation method.

提出了一种基于三维高斯马尔可夫随机场（3D-GMRF）的自适应纹理分割方法。使用估计立方体为给定的体积纹理图像中的每个体素提取特征向量。但是，为该估计立方体选择大小会引起一些基本问题，这些问题与不确定性原理以及模型无法捕获不同纹理图案有关。这些问题在这里通过采用一种自适应方法来解决，其中估计立方体的大小会自适应地变化以捕获不同的图案，并使与估计立方体内与不同纹理类别相关的体素的数量最小化。因此，将由GMRF的估计参数组成并形成参数体积的特征向量用于分割体积纹理。通过使用自适应平均技术应用平均滤波器，可以平滑这些功能。这种平均滤波器极大地改善了分割结果。我们在此提出的方法在合成的体积纹理数据集上进行了评估，并与其他方法进行了比较，以证明我们的分割方法的优越性。