Enhancing joint reconstruction and segmentation with non-convex Bregman iteration
For imaging applications like CT, PET, and MRI, this work offers a joint reconstruction-segmentation approach that outperforms the standard sequential pipeline, though the improvement is incremental.
The paper proposes a unified variational framework combining total variation regularized reconstruction and Chan-Vese segmentation, enhanced by non-convex Bregman iteration, achieving improved reconstruction and segmentation over sequential methods on synthetic and real data.
All imaging modalities such as computed tomography (CT), emission tomography and magnetic resonance imaging (MRI) require a reconstruction approach to produce an image. A common image processing task for applications that utilise those modalities is image segmentation, typically performed posterior to the reconstruction. We explore a new approach that combines reconstruction and segmentation in a unified framework. We derive a variational model that consists of a total variation regularised reconstruction from undersampled measurements and a Chan-Vese based segmentation. We extend the variational regularisation scheme to a Bregman iteration framework to improve the reconstruction and therefore the segmentation. We develop a novel alternating minimisation scheme that solves the non-convex optimisation problem with provable convergence guarantees. Our results for synthetic and real data show that both reconstruction and segmentation are improved compared to the classical sequential approach.