A convex geodesic selective model for image segmentation

Roberts, Michael and Chen, Ke and Irion, Klaus L. (2019) A convex geodesic selective model for image segmentation. Journal of Mathematical Imaging and Vision, 61 (4). pp. 482-503. ISSN 0924-9907 (https://doi.org/10.1007/s10851-018-0857-2)

[thumbnail of Roberts-etal-JMIV-2019-A-convex-geodesic-selective-model-for-image-segmentation]
Preview
 Text. Filename: Roberts-etal-JMIV-2019-A-convex-geodesic-selective-model-for-image-segmentation.pdf
Final Published Version
License: Creative Commons Attribution 4.0 logo
Download (7MB)| Preview

Abstract

Selective segmentation is an important application of image processing. In contrast to global segmentation in which all objects are segmented, selective segmentation is used to isolate specific objects in an image and is of particular interest in medical imaging—permitting segmentation and review of a single organ. An important consideration is to minimise the amount of user input to obtain the segmentation; this differs from interactive segmentation in which more user input is allowed than selective segmentation. To achieve selection, we propose a selective segmentation model which uses the edge-weighted geodesic distance from a marker set as a penalty term. It is demonstrated that this edge-weighted geodesic penalty term improves on previous selective penalty terms. A convex formulation of the model is also presented, allowing arbitrary initialisation. It is shown that the proposed model is less parameter dependent and requires less user input than previous models. Further modifications are made to the edge-weighted geodesic distance term to ensure segmentation robustness to noise and blur. We can show that the overall Euler–Lagrange equation admits a unique viscosity solution. Numerical results show that the result is robust to user input and permits selective segmentations that are not possible with other models.

ORCID iDs

Roberts, Michael, Chen, Ke ORCID logoORCID: https://orcid.org/0000-0002-6093-6623 and Irion, Klaus L.;
  • Item type:Article
    ID code:87231
    Dates:
    Date
    Event
    15 May 2019
    Published
    1 November 2018
    Published Online
    16 October 2018
    Accepted
    17 January 2018
    Submitted
    Subjects:Science > Mathematics
    Department:Faculty of Science > Mathematics and Statistics
    Depositing user: Pure Administrator
    Date deposited:08 Nov 2023 11:31
    Last modified:11 Nov 2024 14:07
    URI:https://strathprints.strath.ac.uk/id/eprint/87231
CORE (COnnecting REpositories)