Wavelet denoising of multiframe optical coherence tomography data

• Verfasst von: Mayer, Markus [VerfasserIn]
• Verfasst von: Wagner, Martin [VerfasserIn]
• Titel: Wavelet denoising of multiframe optical coherence tomography data
• Verf.angabe: Markus A. Mayer, Anja Borsdorf, Martin Wagner, Joachim Hornegger, Christian Y. Mardin, and Ralf P. Tornow
• E-Jahr: 2012
• Jahr: 22 February 2012
• Umfang: 18 S.
• Fussnoten: Gesehen am 05.11.2018
• Titel Quelle: Enthalten in: Biomedical optics express
• Ort Quelle: Washington, DC : OSA, 2010
• Jahr Quelle: 2012
• Band/Heft Quelle: 3(2012), 3, Seite 572-589
• ISSN Quelle: 2156-7085
• DOI: doi:10.1364/BOE.3.000572
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Computed tomography
• Sach-SW: Image processing
• Sach-SW: Image quality
• Sach-SW: Speckle noise
• Sach-SW: Speckle patterns
• Sach-SW: Wavelet transforms
• K10plus-PPN: 1582573441

Abstract

We introduce a novel speckle noise reduction algorithm for OCT images. Contrary to present approaches, the algorithm does not rely on simple averaging of multiple image frames or denoising on the final averaged image. Instead it uses wavelet decompositions of the single frames for a local noise and structure estimation. Based on this analysis, the wavelet detail coefficients are weighted, averaged and reconstructed. At a signal-to-noise gain at about 100% we observe only a minor sharpness decrease, as measured by a full-width-half-maximum reduction of 10.5%. While a similar signal-to-noise gain would require averaging of 29 frames, we achieve this result using only 8 frames as input to the algorithm. A possible application of the proposed algorithm is preprocessing in retinal structure segmentation algorithms, to allow a better differentiation between real tissue information and unwanted speckle noise.