IEEE Trans Vis Comput Graph. 2018 Jan;24(1):964-973. doi: 10.1109/TVCG.2017.2744078. Epub 2017 Aug 29.
In this paper, we propose a novel machine learning-based voxel classification method for highly-accurate volume rendering. Unlike conventional voxel classification methods that incorporate intensity-based features, the proposed method employs dictionary based features learned directly from the input data using hierarchical multi-scale 3D convolutional sparse coding, a novel extension of the state-of-the-art learning-based sparse feature representation method. The proposed approach automatically generates high-dimensional feature vectors in up to 75 dimensions, which are then fed into an intelligent system built on a random forest classifier for accurately classifying voxels from only a handful of selection scribbles made directly on the input data by the user. We apply the probabilistic transfer function to further customize and refine the rendered result. The proposed method is more intuitive to use and more robust to noise in comparison with conventional intensity-based classification methods. We evaluate the proposed method using several synthetic and real-world volume datasets, and demonstrate the methods usability through a user study.
在本文中，我们提出了一种新颖的基于机器学习的体素分类方法，用于高度精确的体绘制。与传统的基于体素分类方法不同，该方法使用基于字典的特征，这些特征是使用分层多尺度 3D 卷积稀疏编码直接从输入数据中学习得到的，这是一种新颖的基于学习的稀疏特征表示方法的扩展。该方法自动生成高达 75 维的高维特征向量，然后将其输入到基于随机森林分类器的智能系统中，以便仅通过用户直接在输入数据上绘制的少量选择笔触，准确地对体素进行分类。我们应用概率传递函数进一步定制和优化渲染结果。与传统的基于强度的分类方法相比，该方法更直观、更能抵抗噪声。我们使用几个合成和真实的体积数据集来评估所提出的方法，并通过用户研究展示了该方法的可用性。
