Probabilistic Slider: A Tool for Visualizing Fuzzy Segmentation Uncertainties

Abstract

Visualization of classification uncertainty of the medical materials is very significant, as different classifications may lead to different visualization results, and different results may cause completely different diagnosis or pre-operative planning decisions, and thus have different consequences to patients. Traditional Direct Volume Rendering (DVR) enables medical experts to visualize the classification uncertainty by random adjustment of the transfer function. However, three main problems exist by using this method: first, the resulting renderings do not indicate any quantitative information about the classification uncertainty of different materials; second, the resulting renderings are randomly revealed by random adjustment of the TF; third, both classification task and optical property assignment task are mixed together in one step. These problems may make this method (1) fail to enable medical experts to make accurate diagnosis or pre-operative planning decision; (2) unable to provide medical experts with a clear concept of how medical volume data are classified. To address these problems, we proposed a probabilistic slider system, which compared to the traditional DVR, enables medical experts to make more accurate diagnosis or pre-operative planning decision, and have a clearer concept of how the medical volume data being classified.