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Vector Quantization for Feature-Preserving Volume Filtering

In volume-rendering applications the input data is usually acquired by measuring some kind of physical property. The accuracy of the measurement strongly influences the quality of images produced by recent 3D visualization techniques. In practice, however, a high signal-to-noise ratio cannot always be ensured. For example, in virtual colonoscopy, CT scans are generated preferably at low radiation dose resulting in noisy volume data. In this paper a novel denoising method is presented, which is based on vector quantization. It is assumed that a highly accurate reference volume is available, like a CT scan acquired at high radiation dose. This reference data is used to calculate a generic codebook for filtering other noisy data sets. The major advantage of this approach is that noise can be efficiently reduced without removing important details.