Magnetic resonance imaging often deals with fast acquisition techniques and incomplete measurements, posing challenges in the reconstruction and further analysis of the data (e.g. segmentation). We propose a method to jointly reconstruct and segment undersampled MRI data. Our model consists of a total variation regularised reconstruction and a Chan-Vese based segmentation. We develop an algorithm based on a splitting approach that solves efficiently the two minimisation subproblems. We present results for synthetic and real data.

This presentation is part of Minisymposium “MS5 - Learning and adaptive approaches in image processing (2 parts)”
organized by: Kostas Papafitsoros (Weierstrass Institute Berlin) , Michael Hintermüller (Humboldt University and Weierstrass Institute Berlin) .

Authors:

Veronica Corona (University of Cambridge)

Keywords:

image reconstruction, image segmentation, inverse problems