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Current Directions in Biomedical Engineering

Joint Journal of the German Society for Biomedical Engineering in VDE and the Austrian and Swiss Societies for Biomedical Engineering

Editor-in-Chief: Dössel, Olaf

Editorial Board: Augat, Peter / Buzug, Thorsten M. / Haueisen, Jens / Jockenhoevel, Stefan / Knaup-Gregori, Petra / Kraft, Marc / Lenarz, Thomas / Leonhardt, Steffen / Malberg, Hagen / Penzel, Thomas / Plank, Gernot / Radermacher, Klaus M. / Schkommodau, Erik / Stieglitz, Thomas / Urban, Gerald A.


CiteScore 2018: 0.47

Source Normalized Impact per Paper (SNIP) 2018: 0.377

Open Access
Online
ISSN
2364-5504
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Implementation and evaluation of segmentation algorithms according to multimodal imaging in personalized medicine

Manuel Stich
  • Corresponding author
  • Institute for diagnostic and interventional radiology, University Hospital Würzburg, 97080 Würzburg, Germany
  • X-Ray & Nuclear Imaging Lab, Institute for Medical Engineering, Ostbayerische Technische Hochschule Amberg-Weiden (OTH), 92637 Weiden, Germany
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  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Jeannine Vogt
  • X-Ray & Nuclear Imaging Lab, Institute for Medical Engineering, Ostbayerische Technische Hochschule Amberg-Weiden (OTH), 92637 Weiden, Germany
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Michaela Lindner
  • X-Ray & Nuclear Imaging Lab, Institute for Medical Engineering, Ostbayerische Technische Hochschule Amberg-Weiden (OTH), 92637 Weiden, Germany
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
/ Ralf Ringler
  • X-Ray & Nuclear Imaging Lab, Institute for Medical Engineering, Ostbayerische Technische Hochschule Amberg-Weiden (OTH), 92637 Weiden, Germany
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2017-09-07 | DOI: https://doi.org/10.1515/cdbme-2017-0178

Abstract

Multimodal imaging is gaining in importance in the field of personalized medicine and can be described as a current trend in medical imaging diagnostics. The segmentation, classification and analysis of tissue structures is essential. The goal of this study is the evaluation of established segmentation methods on different medical image data sets acquired with different diagnostic procedures. Established segmentation methods were implemented using the latest state of the art and applied to medical image data sets. In order to benchmark the segmentation performance quantitatively, medical image data sets were superimposed with artificial Gaussian noise, and the mis-segmentation as a function of the image SNR or CNR was compared to a gold standard. The evaluation of the image segmentation showed that the best results of pixel-based segmentation (< 3%) can be achieved with methods of machine learning, multithreshold and advanced level-set method - even at high artificial noise (SNR< 18). Finally, the complexity of the object geometry and the contrast of the ROI to the surrounding tissue must also be considered to select the best segmentation algorithm.

Keywords: contextual methods; image segmentation; machine learning; medical images; radiology

About the article

Published Online: 2017-09-07


Citation Information: Current Directions in Biomedical Engineering, Volume 3, Issue 2, Pages 207–210, ISSN (Online) 2364-5504, DOI: https://doi.org/10.1515/cdbme-2017-0178.

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©2017 Manuel Stich et al., published by De Gruyter. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. BY-NC-ND 4.0

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