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Dictionary Based Segmentation in Volumes

  • Monica Jane EmersonEmail author
  • Kristine Munk Jespersen
  • Peter Stanley Jørgensen
  • Rasmus Larsen
  • Anders Bjorholm Dahl
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9127)

Abstract

We present a method for supervised volumetric segmentation based on a dictionary of small cubes composed of pairs of intensity and label cubes. Intensity cubes are small image volumes where each voxel contains an image intensity. Label cubes are volumes with voxel-wise probabilities for a given label. The segmentation process is done by matching a cube from the volume, of the same size as the dictionary intensity cubes, to the most similar intensity dictionary cube, and from the associated label cube we get voxel-wise label probabilities. Probabilities from overlapping cubes are averaged and hereby we obtain a robust label probability encoding. The dictionary is computed from labeled volumetric image data based on weighted clustering. We experimentally demonstrate our method using two data sets from material science – a phantom data set of a solid oxide fuel cell simulation for detecting three phases and their interfaces, and a tomogram of a glass fiber composite used in wind turbine blades for detecting individual glass fibers.

Keywords

Volume segmentation Materials images X-ray tomography Learning dictionaries Glass fiber segmentation 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Monica Jane Emerson
    • 1
    Email author
  • Kristine Munk Jespersen
    • 2
  • Peter Stanley Jørgensen
    • 3
  • Rasmus Larsen
    • 1
  • Anders Bjorholm Dahl
    • 1
  1. 1.DTU ComputeLyngbyDenmark
  2. 2.DTU Wind EnergyRoskildeDenmark
  3. 3.DTU EnergyRoskildeDenmark

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