A sparsity-based simplification method for segmentation of spectral-domain optical coherence tomography images
File(s)
Published version
Author(s)
Meiniel, W
Gan, Y
Olivo-Marin, JC
Angelini, E
Type
Conference 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.
Date Issued
2017-08-24
Date Acceptance
2017-08-06
Citation
Proceedings of SPIE - The International Society for Optical Engineering, 2017, 10394
ISBN
9781510612457
ISSN
0277-786X
Publisher
SPIE
Journal / Book Title
Proceedings of SPIE - The International Society for Optical Engineering
Volume
10394
Copyright Statement
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Source
SPIE Optical Engineering + Applications
Publication Status
Published
Start Date
2017-08-06
Finish Date
2017-08-10
Coverage Spatial
San Diego, CA, USA