Gradient-based Classification and Representation of Features from Volume Data

Marius Gavrilescu, Vasile Manta, Meister Eduard Gröller
Gradient-based Classification and Representation of Features from Volume Data
In Proceedings of 15th International Conference on System Theory, Control and computing (ICSTCC 2011), pages 243-248. October 2011.
[Paper]

Information

Abstract

The extraction and representation of information from volume data are important research avenues in computer-based visualization. The interpretation of three- or multi-dimensional data from various scanning devices is important to medical imaging, diagnosis and treatment, reliability and sustainability analyses in various industrial branches, and, in more general terms, information visualization. In this paper, we present several approaches for the classification and representation of relevant information from volume data sets. The techniques are based on the gradient vector, a property directly derived from the original volume data. We show how this property can be computed and subsequently used for classification through gradient-based one- and multi-dimensional transfer functions, as well as for the enhancement of surface features. The described techniques are illustrated through images generated using our volume rendering framework, from Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) data sets. The resulting images show how gradient-based techniques are suited for improved volume classification and the better extraction of meaningful information.

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BibTeX

@inproceedings{Groeller_2011_GBC,
  title =      "Gradient-based Classification and Representation of Features
               from Volume Data",
  author =     "Marius Gavrilescu and Vasile Manta and Meister Eduard
               Gr{"o}ller",
  year =       "2011",
  abstract =   "The extraction and representation of information from volume
               data are important research avenues in computer-based
               visualization. The interpretation of three- or
               multi-dimensional data from various scanning devices is
               important to medical imaging, diagnosis and treatment,
               reliability and sustainability analyses in various
               industrial branches, and, in more general terms, information
               visualization. In this paper, we present several approaches
               for the classification and representation of relevant
               information from volume data sets. The techniques are based
               on the gradient vector, a property directly derived from the
               original volume data. We show how this property can be
               computed and subsequently used for classification through
               gradient-based one- and multi-dimensional transfer
               functions, as well as for the enhancement of surface
               features. The described techniques are illustrated through
               images generated using our volume rendering framework, from
               Computed Tomography (CT) and Magnetic Resonance Imaging
               (MRI) data sets. The resulting images show how
               gradient-based techniques are suited for improved volume
               classification and the better extraction of meaningful
               information.",
  month =      oct,
  booktitle =  "Proceedings of 15th International Conference on System
               Theory, Control and computing (ICSTCC 2011)",
  editor =     "Editura Universitaria Craiova (EUC)",
  issn =       "2068-0465",
  location =   "Sinaia, Romania",
  pages =      "243--248",
  URL =        "https://www.cg.tuwien.ac.at/research/publications/2011/Groeller_2011_GBC/",
}