Augmented reality interaction for semiautomatic volume classification

In the visualization of 3D medical data, the appropriateness of the achieved result is highly dependent on the application. Therefore, an intuitive interaction with the user is of utter importance in order to determine the particular aim of the visualization. In this paper, we present a novel approach for the visualization of 3D medical data with volume rendering combined with AR-based user interaction. The utilization of augmented reality (AR), with the assistance of a set of simple tools, allows the direct manipulation in 3D of the rendered data. The proposed method takes into account regions of interest defined by the user and employs this information to automatically generate an adequate transfer function. Machine learning techniques are utilized for the automatic creation of transfer functions, which are to be used during the classification stage of the rendering pipeline. The validity of the proposed approach for medical applications is illustrated.