Sparsity-based simplification of spectral-domain optical coherence tomography images of cardiac samples

Abstract :

We propose a sparsity-based simplification method for Spectral Domain Optical Coherence Tomography (SD-OCT) images of cardiac samples, displaying layers of tissue. Inspired by the Compressed Sensing (CS) theory, we implement a dedicated sparse sampling of SD-OCT samples achieving image simplification suited for layers segmentation, which is the target application. We validate a straightforward segmentation approach on the variance map of the simplified images against manual delineation on raw SD-OCT images of in-vitro biological samples from four human hearts. We also correlate average layer thickness with histopathological measures. Finally, we compare our simplified images to state of the art denoising approaches.

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https://hal.telecom-paristech.fr/hal-02288466
Contributor : Telecomparis Hal <>
Submitted on : Saturday, September 14, 2019 - 6:51:16 PM
Last modification on : Thursday, October 17, 2019 - 12:37:00 PM

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William Meiniel, Yu Gan, Christine P. Hendon, J.-C. Olivo-Marin, Andrew D. Laine, et al.. Sparsity-based simplification of spectral-domain optical coherence tomography images of cardiac samples. EEE International Symposium on Biomedial Imaging, Apr 2016, Prague, Czech Republic. pp.373-376, ⟨10.1109/ISBI.2016.7493286⟩. ⟨hal-02288466⟩

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