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Accumulation of local maximum intensity for feature enhanced volume rendering

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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.

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Acknowledgements

This work was partly supported by the National Natural Foundation of China (NSFC) project under Grant No. 61070114 and the Zhejiang Provincial Natural Foundation under Grant Nos. Z1090630 and Y1101043.

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Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China

    Ronghua Liang & Yunfei Wu

  2. University of Bedfordshire, Luton, UK

    Feng Dong & Gordon Clapworthy

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  1. Ronghua Liang
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  2. Yunfei Wu
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  3. Feng Dong
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  4. Gordon Clapworthy
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Correspondence to Yunfei Wu.

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Liang, R., Wu, Y., Dong, F. et al. Accumulation of local maximum intensity for feature enhanced volume rendering. Vis Comput 28, 625–633 (2012). https://doi.org/10.1007/s00371-012-0680-5

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