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An Evaluation of Peak Finding for DVR Classification of Biological Data

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Part of the Mathematics and Visualization book series (MATHVISUAL)

Abstract

In medicine and the life sciences, volume data are frequently entropic, containing numerous features at different scales as well as significant noise from the scan source. Conventional transfer function approaches for direct volume rendering have difficulty handling such data, resulting in poor classification or undersampled rendering. Peak finding addresses issues in classifying noisy data by explicitly solving for isosurfaces at desired peaks in a transfer function. As a result, one can achieve better classification and visualization with fewer samples and correspondingly higher performance. This paper applies peak finding to several medical and biological data sets, particularly examining its potential in directly rendering unfiltered and unsegmented data.

Keywords

  • Transfer Function
  • Volume Rendering
  • Anisotropic Diffusion
  • Sharp Feature
  • Peak Find

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Aaron Knoll .

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Knoll, A., Westerteiger, R., Hagen, H. (2012). An Evaluation of Peak Finding for DVR Classification of Biological Data. In: Linsen, L., Hagen, H., Hamann, B., Hege, HC. (eds) Visualization in Medicine and Life Sciences II. Mathematics and Visualization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21608-4_6

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