Information-based transfer functions for multimodal visualization

摘要: Transfer functions are an essential part of volume visualization. In multimodal visualization at least two values exist every sample point. Additionally, other parameters, such as gradient magnitude, often retrieved for each To find a good transfer function this high number parameters is challenging because the complexity task. paper we present general information-based approach design in which independent used modality types. Based on information theory, complex multi-dimensional space fused to allow utilization well-known 2D with single value and magnitude parameters. quantity introduced enables better separation regions complementary information. The benefit new method contrast techniques easy understand provides different tissues. usability shown examples modalities.