In many applications of X-ray computed tomography, an unsupervised segmentation of the reconstructed 3D volumes forms an important step in the image processing chain for further investigation of the digitized object. Therefore, the goal is to train a clustering algorithm on the volume, which produces a voxelwise classification by assigning a cluster index to each voxel. However, clustering methods, e.g., K-Means, typically have an asymptotic polynomial runtime with respect to the dataset size, and thus, these techniques are rarely applicable to large volumes. In this work, we introduce a novel clustering technique based on random sampling, which allows for the voxelwise classification of arbitrarily large volumes. The presented method conducts efficient linear passes over the data to extract a representative random sample of a fixed size on which the classifier can be trained. Then, a final linear pass performs the segmentation and assigns a cluster index to each individual voxel. Quantitative and qualitative evaluations show that excellent results can be achieved even with a very small sample size. Consequently, the unsupervised segmentation by means of clustering becomes feasible for arbitrarily large volumes.
翻译:在X射线计算断层法的许多应用中,对重建的3D卷进行未经监督的分割是图像处理链中进一步调查数字化对象的一个重要步骤,因此,目标是对体积进行群集算法培训,通过为每个反毒性者指定一个群集指数来产生一个反氧化素分类。然而,群集方法,例如K-Means,通常在数据集大小方面有一个无症状的多元运行时间,因此,这些技术很少适用于大体积。在这项工作中,我们采用了一种基于随机抽样的新型群集技术,允许对任意大体进行反氧化素分类。因此,所提出的方法对数据进行了高效的线性传输,以提取具有代表性的、固定大小的随机样本,供分类者加以培训。然后,最后的线性通过对每个反毒者进行分解,并给每个个人指定一个群集指数。定量和定性评估表明,即使抽样大小很小,也能够取得优异的结果。因此,任意大体体积采用未受监视的分组分割法是可行的。</s>