Parameter-free selective segmentation with convex variational methods

Research output: Contribution to journalArticle


Colleges, School and Institutes


Selective segmentation methods involve incorporating user input to partition an image into a foreground and background. These methods are often sensitive to some aspect of the user input in a counter intuitive manner, making their use in practice difficult. The most robust methods often involve laborious refinement on the part of the user, and sometimes editing/supervision. The proposed method reduces the burden of the user by simplifying the requirements in the input. Specifically, the fitting term does not depend on a distance function, and so no selection parameter is introduced. Instead, we consider how the user input relates to some general intensity fitting term to ensure the approach is less sensitive to the decisions or intuition of the user. We give comparisons to existing approaches to show the advantages of the new selective segmentation model.


Original languageEnglish
Pages (from-to)2163-2172
Number of pages10
JournalIEEE Transactions on Image Processing
Issue number5
Early online date28 Nov 2018
Publication statusPublished - May 2019


  • Image segmentation, mathematical model, biomedical imaging, computed tomography