Enabling Haptic Interaction with Volumetric MRI Data Through Knowledge-based Tissue Separation

Direct volume haptics can provide both guidance and extra information during exploration of volumetric data. In this paper we present a novel approach to volume haptics enabling haptic exploration of tissue shape, borders and material properties in data despite low contrast and low signal to noise ratio, as is common in medical MRI data or low dose CT data. The method uses filtering based on implicit knowledge and addresses the problem of overlapping scalar ranges through the introduction of fuzzy classification and corresponding transfer functions for material properties as well as classification-based distance masking for haptic force direction.