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A sparsity-based simplification method for segmentation of spectral-domain optical coherence tomography images
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Paper Abstract

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.

Paper Details

Date Published: 24 August 2017
PDF: 8 pages
Proc. SPIE 10394, Wavelets and Sparsity XVII, 1039406 (24 August 2017); doi: 10.1117/12.2274126
Show Author Affiliations
William Meiniel, Institut Pasteur, CNRS (France)
Institut Telecom, Univ. Paris-Saclay (France)
Yu Gan, Columbia Univ. (United States)
Jean-Christophe Olivo-Marin, Institut Pasteur, CNRS (France)
Elsa Angelini, Institut Telecom, Univ. Paris-Saclay (France)
Imperial College London (United Kingdom)


Published in SPIE Proceedings Vol. 10394:
Wavelets and Sparsity XVII
Yue M. Lu; Dimitri Van De Ville; Manos Papadakis, Editor(s)

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