Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images.
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.
Leyuan
L
Fang
Shutao
S
Li
David
D
Cunefare
Sina
S
Farsiu
D000465:Algorithms; D006801:Humans; D012160:Retina; D041623:Tomography, Optical Coherence
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