Two-Level Transfer Functions Using t-SNE for Data Segmentation in Direct Volume Rendering.

Sangbong Yoo, Seokyeon Kim, Yun Jang
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Abstract

The transfer function (TF) design is crucial for enhancing the visualization quality and understanding of volume data in volume rendering. Recent research has proposed various multidimensional TFs to utilize diverse attributes extracted from volume data for controlling individual voxel rendering. Although multidimensional TFs enhance the ability to segregate data, manipulating various attributes for the rendering is cumbersome. In contrast, low-dimensional TFs are more beneficial as they are easier to manage, but separating volume data during rendering is problematic. This paper proposes a novel approach, a two-level transfer function, for rendering volume data by reducing TF dimensions. The proposed technique involves extracting multidimensional TF attributes from volume data and applying t-Stochastic Neighbor Embedding (t-SNE) to the TF attributes for dimensionality reduction. The two-level transfer function combines the classical 2D TF and t-SNE TF in the conventional direct volume rendering pipeline. The proposed approach is evaluated by comparing segments in t-SNE TF and rendering images using various volume datasets. The results of this study demonstrate that the proposed approach can effectively allow us to manipulate multidimensional attributes easily while maintaining high visualization quality in volume rendering.

利用 t-SNE 在直接体积渲染中进行数据分割的两级传递函数
转移函数(TF)的设计对于在体绘制中提高可视化质量和理解体数据至关重要。最近的研究提出了各种多维传递函数,以利用从体数据中提取的各种属性来控制单个体素的渲染。虽然多维 TF 增强了数据分离的能力,但操作各种属性进行渲染非常麻烦。相比之下,低维 TF 更为有利,因为它们更易于管理,但在渲染过程中分离体素数据却存在问题。本文提出了一种新方法--两级传递函数,通过降低 TF 维度来渲染体积数据。建议的技术包括从体积数据中提取多维 TF 属性,并对 TF 属性应用 t-Stochastic Neighbor Embedding(t-SNE)进行降维。在传统的直接体积渲染管道中,两级传递函数结合了经典的二维 TF 和 t-SNE TF。通过比较 t-SNE TF 中的片段和使用各种体积数据集渲染的图像,对所提出的方法进行了评估。研究结果表明,所提出的方法可以有效地让我们轻松处理多维属性,同时在体积渲染中保持较高的可视化质量。
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