Abstract

Image segmentation is an important subtask in biomedical research applications, such as estimating the position and shape of a tumor. Unfortunately, advanced image segmentation methods are not widely applied in research applications as they often miss features, such as uncertainty communication, and may lack an intuitive approach for the use of the underlying algorithm. To solve this problem, this work fuses a fuzzy and a hierarchical segmentation approach together, thus providing a flexible multi-class segmentation method based on probabilistic path propagations. By utilizing this method, analysts and physicians can map their mental model of image components and their composition to higher level objects. The probabilistic segmentation of higher order components is propagated along the user-defined hierarchy to highlight the potential of improvement resulting in each level of hierarchy by providing an intuitive representation. The effectiveness of this approach is demonstrated by evaluating our segmentations of biomedical datasets, comparing it to state of the art segmentation approaches, and an extensive user study.

Original languageEnglish (US)
JournalIEEE Computer Graphics and Applications
DOIs
StateAccepted/In press - Jan 1 2019

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Image segmentation
Semantics
Electric fuses
Tumors
Communication
Chemical analysis
Uncertainty

Keywords

  • Biomedical imaging
  • Cognitive science
  • Image segmentation
  • Motion segmentation
  • Probabilistic logic
  • Semantics
  • Uncertainty

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design

Cite this

Hierarchical Image Semantics using Probabilistic Path Propagations for Biomedical Research. / Gillmann, Christina; Post, Tobias; Wischgoll, Thomas; Hagen, Hans; Maciejewski, Ross.

In: IEEE Computer Graphics and Applications, 01.01.2019.

Research output: Contribution to journalArticle

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