Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images.


Journal Article

We demonstrate the usefulness of utilizing a segmentation step for improving the performance of sparsity based image reconstruction algorithms. In specific, we will focus on retinal optical coherence tomography (OCT) reconstruction and propose a novel segmentation based reconstruction framework with sparse representation, termed segmentation based sparse reconstruction (SSR). The SSR method uses automatically segmented retinal layer information to construct layer-specific structural dictionaries. In addition, the SSR method efficiently exploits patch similarities within each segmented layer to enhance the reconstruction performance. Our experimental results on clinical-grade retinal OCT images demonstrate the effectiveness and efficiency of the proposed SSR method for both denoising and interpolation of OCT images.

Full Text

Duke Authors

Cited Authors

  • Fang, L; Li, S; Cunefare, D; Farsiu, S

Published Date

  • February 2017

Published In

Volume / Issue

  • 36 / 2

Start / End Page

  • 407 - 421

PubMed ID

  • 27662673

Pubmed Central ID

  • 27662673

Electronic International Standard Serial Number (EISSN)

  • 1558-254X

International Standard Serial Number (ISSN)

  • 0278-0062

Digital Object Identifier (DOI)

  • 10.1109/tmi.2016.2611503


  • eng