A porosity model for medical image segmentation of vessels

Vahid Goodarzi Ardakani, Alberto M Gambaruto*, Goncalo Silva, Ricardo Pereira

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

4 Citations (Scopus)
95 Downloads (Pure)


A physics-based medical image segmentation method is developed. Specifically, the image greyscale intensity is used to infer the voxel partial volumes and subsequently formulate a porous medium analogy. The method involves first translating the medical image volumetric data into a three-dimensional computational domain of a porous material. A velocity field is then obtained from numerical simulations of incompressible fluid flow in the porous material, and finally a velocity iso-surface provides the surface description of the target object. The approach is tested on CT images of eight patient-specific cases, where cerebral aneurysms, nasal cavities (NC), and an aortic arch (AA) are the objects of interest. In the aneurysm cases, the results are compared against constant greyscale thresholding and manual segmentation. The manual segmentations of the aneurysms are validated by a clinical practitioner. Only a qualitative comparison is available for the NC, and the AA geometries. The results show that the proposed method is effective and capable of extracting the target object in a noisy domain. A sensitivity study is carried out to verify the method's performance with respect to modelling or user choices. The segmentation by the proposed method is also evaluated by performing computational fluid dynamics simulation, including a near-wall flow analysis, to ensure that the segmented geometry and the resulting computed solution are representative and meaningful.
Original languageEnglish
Article numbere3580
Number of pages32
JournalInternational Journal for Numerical Methods in Biomedical Engineering
Issue number4
Early online date9 Feb 2022
Publication statusE-pub ahead of print - 9 Feb 2022

Bibliographical note

Funding Information:
Vahid Goodarzi Ardakani gratefully acknowledges support from the University of Bristol to carry out his PhD studies.Goncalo Silva thanks the support of FCT, through IDMEC, under LAETA, project UIDB/50022/2020. The authors would also like to thank Centro Hospitalar e Universitário de Coimbra for the medical data. The authors would like to thank Prof. Jorge Tiago and Dr. Iolanda Velho at Instituto Superior Técnico, University of Lisbon, Portugal, for supporting this study and overseeing the image segmentation for the cerebral aneurysm datasets.

Publisher Copyright:
© 2022 The Authors. International Journal for Numerical Methods in Biomedical Engineering published by John Wiley & Sons Ltd.


  • aortic arch
  • cerebral aneurysm
  • computational fluid dynamics
  • medical imagesegmentation
  • nasal cavity
  • porous medium
  • velocity thresholding
  • viscous resistance


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