A generalized asymmetric dual-front model for active contours and image segmentation

Chen, Da and Spencer, Jack and Mirebeau, Jean-Marie and Chen, Ke and Shu, Minglei and Cohen, Laurent D. (2021) A generalized asymmetric dual-front model for active contours and image segmentation. IEEE Transactions on Image Processing, 30. pp. 5056-5071. ISSN 1057-7149 (https://doi.org/10.1109/TIP.2021.3078102)

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Abstract

The Voronoi diagram-based dual-front scheme is known as a powerful and efficient technique for addressing the image segmentation and domain partitioning problems. In the basic formulation of existing dual-front approaches, the evolving contour can be considered as the interfaces of adjacent Voronoi regions. Among these dual-front models, a crucial ingredient is regarded as the geodesic metrics by which the geodesic distances and the corresponding Voronoi diagram can be estimated. In this paper, we introduce a new dual-front model based on asymmetric quadratic metrics. These metrics considered are built by the integration of the image features and a vector field derived from the evolving contour. The use of the asymmetry enhancement can reduce the risk for the segmentation contours being stuck at false positions, especially when the initial curves are far away from the target boundaries or the images have complicated intensity distributions. Moreover, the proposed dual-front model can be applied for image segmentation in conjunction with various region-based homogeneity terms. The numerical experiments on both synthetic and real images show that the proposed dual-front model indeed achieves encouraging results.

ORCID iDs

Chen, Da, Spencer, Jack, Mirebeau, Jean-Marie, Chen, Ke ORCID logoORCID: https://orcid.org/0000-0002-6093-6623, Shu, Minglei and Cohen, Laurent D.;
  • Item type:Article
    ID code:87444
    Dates:
    Date
    Event
    12 May 2021
    Published
    3 May 2021
    Accepted
    31 May 2020
    Submitted
    Notes:Copyright © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
    Subjects:Science > Mathematics > Electronic computers. Computer science
    Science > Mathematics
    Department:Faculty of Science > Mathematics and Statistics
    Depositing user: Pure Administrator
    Date deposited:27 Nov 2023 11:20
    Last modified:19 Nov 2024 03:30
    URI:https://strathprints.strath.ac.uk/id/eprint/87444
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