69,044 research outputs found
Enhancing joint reconstruction and segmentation with non-convex Bregman iteration
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
A sparsity-based simplification method for segmentation of spectral-domain optical coherence tomography images
Optical coherence tomography (OCT) has emerged as a promising image modality to characterize biological tissues. With axio-lateral resolutions at the micron-level, OCT images provide detailed morphological information and enable applications such as optical biopsy and virtual histology for clinical needs. Image enhancement is typically required for morphological segmentation, to improve boundary localization, rather than enrich detailed tissue information. We propose to formulate image enhancement as an image simplification task such that tissue layers are smoothed while contours are enhanced. For this purpose, we exploit a Total Variation sparsity-based image reconstruction, inspired by the Compressed Sensing (CS) theory, but specialized for images with structures arranged in layers. We demonstrate the potential of our approach on OCT human heart and retinal images for layers segmentation. We also compare our image enhancement capabilities to the state-of-the-art denoising techniques
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