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

24 August 2017 A sparsity-based simplification method for segmentation of spectral-domain optical coherence tomography images

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

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Proceedings Volume 10394, Wavelets and Sparsity XVII; 1039406 (2017) https://doi.org/10.1117/12.2274126
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

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.

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William Meiniel, Yu Gan, Jean-Christophe Olivo-Marin, and Elsa Angelini "A sparsity-based simplification method for segmentation of spectral-domain optical coherence tomography images", Proc. SPIE 10394, Wavelets and Sparsity XVII, 1039406 (24 August 2017); https://doi.org/10.1117/12.2274126

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