Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images.
Published
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
Language
- eng