TY - GEN
T1 - A sparsity-based simplification method for segmentation of spectral-domain optical coherence tomography images
AU - Meiniel, William
AU - Gan, Yu
AU - Olivo-Marin, Jean Christophe
AU - Angelini, Elsa
N1 - Publisher Copyright:
© 2017 SPIE.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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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U2 - 10.1117/12.2274126
DO - 10.1117/12.2274126
M3 - Conference contribution
AN - SCOPUS:85033585119
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Wavelets and Sparsity XVII
A2 - Lu, Yue M.
A2 - Van De Ville, Dimitri
A2 - Van De Ville, Dimitri
A2 - Papadakis, Manos
T2 - Wavelets and Sparsity XVII 2017
Y2 - 6 August 2017 through 9 August 2017
ER -