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Gaussian mixture model-based target feature extraction and visualization

机译：高斯混合模型的目标特征提取和可视化

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摘要

Effective extraction and visualization of complex target features from volume data are an important task, which allows the user to analyze and get insights of the complex features, and thus to make reasonable decisions. Some state-of-the-art techniques allow the user to perform such a task by interactively exploring in multiple linked parameter space views. However, interactions in the parameter space using trial and error may be unintuitive and time-consuming. Furthermore, switching between multiple views may be distracting. Some other state-of-the-art techniques allow the user to extract the complex features by directly interacting in the data space and subsequently visualize the extracted features in 3D view. They are intuitive and effective techniques for the user, as the user is familiar with the data space, and they do not require many trial and error to get the features. However, these techniques usually generate less accurate features. In this paper, we proposed a semiautomatic Gaussian mixture model-based target feature extraction and visualization method, which allows the user to quickly label single or multiple complex target features using lasso on two slices of the volume data and subsequently visualize the automatically extracted features in 3D view. We have applied it to various univariate or multivariate volume datasets from the medical field to demonstrate its effectiveness. Moreover, we have performed both qualitative and quantitative experiments to compare its results against the results from two state-of-the-art techniques and the ground truths. The experimental results showed that our method is able to generate the closest results to the ground truth.
机译：来自卷数据的复杂目标特征的有效提取和可视化是一个重要任务，允许用户分析和获取复杂特征的见解，从而做出合理的决策。一些最先进的技术允许用户通过在多个链接参数空间视图中交互探索来执行这样的任务。但是，使用试验和错误的参数空间中的相互作用可能是不可行的和耗时的。此外，在多视图之间切换可能会分散注意力。一些其他最先进的技术允许用户通过在数据空间中直接交互并随后在3D视图中可视化提取的特征来提取复杂特征。它们是用户的直观和有效的技术，因为用户熟悉数据空间，并且它们不需要许多试验和错误来获取功能。但是，这些技术通常会产生较少的准确功能。在本文中，我们提出了一种基于半自动的高斯混合模型的目标特征提取和可视化方法，其允许用户在卷数据的两片上使用套索快速标记单个或多个复杂目标功能，并随后可视化自动提取的功能3d视图。我们已将其应用于医疗领域的各种单变量或多变量体积数据集，以展示其有效性。此外，我们已经进行了定性和定量实验，以将其结果与来自两种最新技术和地面真理的结果进行比较。实验结果表明，我们的方法能够将最接近的结果生成到地面真理。

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  • 来源
    《Journal of visualization》 |2021年第3期|545-563|共19页
  • 作者

    Ji Ma; Jinjin Chen; Liye Chen; Xingjian Zhou; Xujia Qin; Ying Tang; Guodao Sun; Jiazhou Chen;

  • 作者单位

    School of Computer Science and Technology Zhejiang University of Technology Hangzhou China;

    School of Design and Art Communication University of Zhejiang Hangzhou China;

    Sir Run Run Shaw Hospital School of Medicine Zhejiang University Hangzhou China;

    School of Computer Science and Technology Zhejiang University of Technology Hangzhou China;

    School of Computer Science and Technology Zhejiang University of Technology Hangzhou China;

    School of Computer Science and Technology Zhejiang University of Technology Hangzhou China;

    School of Computer Science and Technology Zhejiang University of Technology Hangzhou China;

    School of Computer Science and Technology Zhejiang University of Technology Hangzhou China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Feature extraction; Visualization; Gaussian mixture model; Volume data;

    机译：特征提取;可视化;高斯混合模型;卷数据;

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