Accumulation of local maximum intensity for feature enhanced volume rendering

Abstract

Maximum Intensity Difference Accumulation (MIDA) combines the advantage of Direct Volume Rendering (DVR) and Maximum Intensity Projection (MIP). However, many features with local maximum intensity are still missing in the final rendering image. This paper presents a novel approach to focus on features with local maximum intensity within the dataset. Moving Least Squares (MLS) is used to smooth each ray profile during the raycasting in order to eliminate noise in the data and to highlight significant transition points on the profile. We then adopt a local minimum-point searching method to analyze the ray profile, and identify the transition points that mark the local maximum intensity points within the dataset. At the rendering stage, we implement a novel local intensity difference accumulation (LIDA) to accumulate the colors and opacity. Surface shading is introduced to improve the spatial cues of the features. We also employ tone-reduction to preserve the original local contrast. Our approach can highlight local features in the dataset without involving the adjustment of transfer functions. The experiments demonstrate high-quality rendering results at an interactive frame rate.