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

William Meiniel, Yu Gan, Jean Christophe Olivo-Marin, Elsa Angelini

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

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.

Original languageEnglish
Title of host publicationWavelets and Sparsity XVII
EditorsYue M. Lu, Dimitri Van De Ville, Dimitri Van De Ville, Manos Papadakis
ISBN (Electronic)9781510612457
DOIs
StatePublished - 2017
EventWavelets and Sparsity XVII 2017 - San Diego, United States
Duration: 6 Aug 20179 Aug 2017

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume10394
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceWavelets and Sparsity XVII 2017
Country/TerritoryUnited States
CitySan Diego
Period6/08/179/08/17

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