A new approach using the Beltrami repre- sentation of a shape for topology preserving image seg- mentation is proposed. Using the proposed model, the target object can be segmented from the input image by a region of user prescribed topology. Given a target image I, a template image J is constructed and then deformed with respect to the Beltrami representation. The deformation on J is designed such that the topology of the segmented region is preserved as which the object interior in J. The topology preserving property of the deformation is guaranteed by imposing only one constraint on the Beltrami representation, which is easy to be handled. Introducing the Beltrami representation also allows large deformations on the topological prior J, so that it can be a very simple image, such as an image of disks, torus, disjoint disks, etc. Hence, prior shape information of I is unnecessary for the proposed model. Additionally, the proposed model can be easily incorporated with selective segmentation, in which landmark constraints can be imposed interactively to meet any practical need (e.g. medical imaging). High accuracy and stability of the proposed model to deal with different segmentation tasks are validated by nu- merical experiments on both artificial and real images.

This presentation is part of Minisymposium “MS22 - Mapping problems in imaging, graphics and vision (3 parts)”
organized by: Ronald Lui (Chinese University of Hong Kong) , Ke Chen (University of Liverpool) .

Authors:

Hei Long Chan (The Chinese University of Hong Kong)

Keywords:

image segmentation