A variational joint segmentation and registration framework for multimodal images

Ademaj, Adela and Rada, Lavdie and Ibrahim, Mazlinda and Chen, Ke (2020) A variational joint segmentation and registration framework for multimodal images. Journal of Algorithms and Computational Technology, 14. pp. 1-9. ISSN 1748-3026 (https://doi.org/10.1177/1748302620966691)

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Abstract

Image segmentation and registration are closely related image processing techniques and often required as simultaneous tasks. In this work, we introduce an optimization-based approach to a joint registration and segmentation model for multimodal images deformation. The model combines an active contour variational term with mutual information (MI) smoothing fitting term and solves in this way the difficulties of simultaneously performed segmentation and registration models for multimodal images. This combination takes into account the image structure boundaries and the movement of the objects, leading in this way to a robust dynamic scheme that links the object boundaries information that changes over time. Comparison of our model with state of art shows that our method leads to more consistent registrations and accurate results.

ORCID iDs

Ademaj, Adela, Rada, Lavdie, Ibrahim, Mazlinda and Chen, Ke ORCID logoORCID: https://orcid.org/0000-0002-6093-6623;
  • Item type:Article
    ID code:87258
    Dates:
    Date
    Event
    31 December 2020
    Published
    17 November 2020
    Published Online
    26 September 2020
    Accepted
    Subjects:Science > Mathematics
    Department:UNSPECIFIED
    Depositing user: Pure Administrator
    Date deposited:09 Nov 2023 11:40
    Last modified:11 Nov 2024 14:07
    URI:https://strathprints.strath.ac.uk/id/eprint/87258
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