Sparsity based denoising of spectral domain optical coherence tomography images
| Autor: | Leyuan Fang, Shutao Li, Joseph A. Izatt, Cynthia A. Toth, Qing Nie, Sina Farsiu |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2012 |
| Předmět: |
Computer science
Image quality Noise reduction Physics::Medical Physics Image processing 02 engineering and technology 01 natural sciences ocis:(170.5755) Retina scanning 010309 optics Optical coherence tomography ocis:(100.2980) Image enhancement 0103 physical sciences 0202 electrical engineering electronic engineering information engineering medicine ocis:(170.4460) Ophthalmic optics and devices Computer vision ocis:(100.0100) Image processing ocis:(030.4280) Noise in imaging systems medicine.diagnostic_test business.industry Speckle noise Sparse approximation ocis:(110.4500) Optical coherence tomography Atomic and Molecular Physics and Optics Data set Compressed sensing Computer Science::Computer Vision and Pattern Recognition 020201 artificial intelligence & image processing Artificial intelligence business Image Reconstruction and Inverse Problems Biotechnology |
| Zdroj: | Biomedical Optics Express |
| ISSN: | 2156-7085 |
| Popis: | In this paper, we make contact with the field of compressive sensing and present a development and generalization of tools and results for reconstructing irregularly sampled tomographic data. In particular, we focus on denoising Spectral-Domain Optical Coherence Tomography (SDOCT) volumetric data. We take advantage of customized scanning patterns, in which, a selected number of B-scans are imaged at higher signal-to-noise ratio (SNR). We learn a sparse representation dictionary for each of these high-SNR images, and utilize such dictionaries to denoise the low-SNR B-scans. We name this method multiscale sparsity based tomographic denoising (MSBTD). We show the qualitative and quantitative superiority of the MSBTD algorithm compared to popular denoising algorithms on images from normal and age-related macular degeneration eyes of a multi-center clinical trial. We have made the corresponding data set and software freely available online. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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