A feature-based transfer function for liver visualization

This paper presents a new method for the automatic generation of patient-specific, feature-based multi-dimensional transfer functions used in the simultaneous visualization of liver blood vessels and tumors in CT datasets. The method automatically extracts the geometrical structure of the vessels and tumors with a multi-scale eigen-analysis of the image Hessian matrix. It then uses this information to optimize the transfer function based on energy minimization in a variational framework. The method overcomes key drawbacks of existing volume visualization techniques, which are limited to predefined transfer functions, require lengthy manual adjustment based on CT iso-values, and often produce suboptimal results, especially when different types of structures of interest are involved. We demonstrate the method on five clinical data sets with about 100 slices, obtained transfer functions for each in about 90 s, and produced real-time visualizations. The visualizations were evaluated and compared to those obtained by two existing methods by an expert radiologist, who judged them superior in detail and specificity.