基于概率分类模型的体数据分类可视化

Volume Data Classification Visualization Based on Probabilistic Classification Model

摘要: 分类可视化能够直观表达体数据的结构特征，是研究体数据的重要方法。本文提出一种基于概率分类模型的体数据分类可视化方法。该方法首先建立体素的概率分类模型，利用灰度直方图中定义的传递函数实现体素的分类。然后将概率分类模型与核回归重构相结合，获得更加准确地重构结果，并构造重构点的重构有效性。最后结合重构有效性实现从灰度值到颜色、不透明度的可视化映射，得到最终的绘制结果。实验表明，本文方法能够有效区分体数据中的不同结构特征，形成同质连续、异质分离的绘制结果，实现较好的分类可视化效果。

Abstract: Classification visualization is an important method for volume data study, which can intuitively reveal the structural characteristics. This paper proposes a classification visualization method based on probabilistic classification model. First, the voxel probabilistic classification model is established, which realizes the classification of voxel with the transfer function defined in gray histogram. Then voxel probabilistic classification model and kernel regression are combined to achieve more accurate reconstruction result, and the validity of reconstruction on reconstructed points is measured. Finally, from gray value to color and opacity, the visualization mapping based on reconstruction validity is implemented to obtain the final visualization result. The experiment shows that the proposed method can efficiently separate the feature structures of volume data and achieve a better classification visualization result of homogeneous continuity and heterogeneous separation.