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Hyperspectral Image Data for Diagnosis of Endometriosis

Fast facts

  • Internal authorship

  • Further publishers

    Stefan Patzke, Christian Wöhler

  • Publishment

    • 2025
    • Volume Proceedings of the 11th International Conference on Bioinformatics Research and Applications
  • Title of the conference proceedings

    Proceedings of the 11th International Conference on Bioinformatics Research and Applications

  • Organizational unit

  • Subjects

    • Civil engineering in general
    • Biomedical technology
    • Ingenieurinformatik/Technische Informatik
    • Engineering sciences in general
  • Research fields

    • BioMedicalTechnology (BMT)
  • Publication format

    Conference paper

Quote

S. Patzke, C. Wöhler, and J. Thiem, "Hyperspectral Image Data for Diagnosis of Endometriosis," in Proceedings of the 11th International Conference on Bioinformatics Research and Applications, 2025, pp. 30-34.

Content

This research focuses on the question whether the use of hyperspectral imaging sensors, in comparison to conventional RGB sensors, offers advantages to assist surgeons during minimally invasive procedures and to increase therapy quality by preventing clinical recurrences in laparoscopic endometriosis diagnostics. Since hyperspectral data for medical applications are not publicly available, the acquisition of suitable data is essential for the realization of this research. Therefore, the method of data collection is addressed in this work. Additionally, results based on the current data collection using Principal Component Analysis (PCA) for dimensionality reduction and Self-Organizing-Map (SOM) for clustering are presented. In this way, spectral differences can be extracted from the data that appear to be visually similar.

Notes and references

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