Abstract

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.

Graphic abstract

中文翻译：

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基于高斯混合模型的目标特征提取与可视化

摘要

从体积数据中有效提取和可视化复杂目标特征是一项重要任务，它允许用户分析并了解复杂特征，从而做出合理的决策。一些最新技术允许用户通过在多个链接的参数空间视图中进行交互探索来执行此类任务。但是，使用反复试验在参数空间中进行交互可能是不直观且耗时的。此外，在多个视图之间切换可能会分散注意力。其他一些最新技术允许用户通过直接在数据空间中进行交互来提取复杂特征，然后在3D视图中可视化提取的特征。对于用户而言，它们是直观而有效的技术，因为用户熟悉数据空间，并且他们不需要很多试验和错误就能获得这些功能。但是，这些技术通常会产生不太准确的特征。在本文中，我们提出了一种基于半自动高斯混合模型的目标特征提取和可视化方法，该方法允许用户使用套索在两个体数据上快速标记单个或多个复杂目标特征，然后可视化自动提取的特征。 3D视图。我们将其应用于医学领域的各种单变量或多变量体积数据集，以证明其有效性。此外，我们已经进行了定性和定量实验，以将其结果与两种最新技术和真实情况的结果进行比较。

图形摘要