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..
| Medienart: |
Artikel |
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| Erscheinungsjahr: |
2017 |
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| Erschienen: |
2017 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:36 |
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| Enthalten in: |
IEEE transactions on medical imaging - 36(2017), 2, Seite 407-421 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Fang, Leyuan [VerfasserIn] |
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| Links: |
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| RVK: |
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| doi: |
10.1109/TMI.2016.2611503 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
OLC1990934307 |
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| 520 | |a 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. | ||
| 650 | 4 | |a layer segmentation | |
| 650 | 4 | |a Image resolution | |
| 650 | 4 | |a Retina | |
| 650 | 4 | |a optical coherence tomography | |
| 650 | 4 | |a Denoising | |
| 650 | 4 | |a ophthalmic imaging | |
| 650 | 4 | |a Dictionaries | |
| 650 | 4 | |a Image segmentation | |
| 650 | 4 | |a Noise reduction | |
| 650 | 4 | |a Image reconstruction | |
| 650 | 4 | |a sparse representation | |
| 650 | 4 | |a Interpolation | |
| 700 | 1 | |a Li, Shutao |4 oth | |
| 700 | 1 | |a Cunefare, David |4 oth | |
| 700 | 1 | |a Farsiu, Sina |4 oth | |
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