Concentric Circle Glyphs for Enhanced Depth-Judgment in Vascular Models

Abstract

Using 3D models of medical data for surgery or treatment planning requires a comprehensive visualization of the data. This is crucial to support the physician in creating a cognitive image of the presented model. Vascular models are complex structures and, thus, the correct spatial interpretation is difficult. We propose view-dependent circle glyphs that enhance depth perception in vascular models. The glyphs are automatically placed on vessel end-points in a balanced manner. For this, we introduce a vessel end-point detection algorithm as a pre-processing step and an extensible, feature-driven glyph filtering strategy. Our glyphs are simple to implement and allow an enhanced and quick judgment of the depth value that they represent. We conduct a qualitative evaluation to compare our approach with two existing approaches, that enhance depth perception with illustrative visualization techniques. The evaluation shows that our glyphs perform better in the general case and decisively outperform the reference techniques when it comes to just noticeable differences.