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Shahab Chitchian, Markus A. Mayer, Adam Boretsky, Frederik van Kuijk, Massoud Motamedi; Complex Wavelet Denoising of Retinal OCT Imaging. Invest. Ophthalmol. Vis. Sci. 2012;53(14):3124.
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© ARVO (1962-2015); The Authors (2016-present)
A denoising algorithm using double-density dual-tree complex wavelet transform is applied to two-frame optical coherence tomography (OCT) images of the retina, the original images from a clinical system without preprocessing, to improve the image quality. The algorithm overcomes the limitations of commonly used multiple frame averaging technique, namely the limited number of frames that can be recorded due to eye movements, by providing a comparable image quality in less acquisition time.
Speckle noise as a dominant source of artifacts occurs in the OCT image because particles composed in the underlying tissue structures are smaller than the coherence length of the light source. Wavelet denoising performs shrinking in the wavelet transform domain. The double-density dual-tree complex wavelet transform (DD-CDWT) is a combination of the double-density wavelet transform (DD-DWT) and the dual-tree complex wavelet transform (CDWT), each of which has its own characteristics and advantages. In this study, a locally adaptive denoising algorithm using DD-CDWT is applied to reduce speckle noise in two-frame OCT images of the retina.
7 linear B-Scans of the macula region (768 A-Scans) were acquired from a subject. These 7 B-Scans are two-frame averaged images, the original images from Spectralis OCT system (Heidelberg Engineering, Heidelberg, Germany). Applying DD-CDWT provides improved results for speckle noise reduction in OCT images in comparison to CDWT. The retinal structure can be separated from intensity variations due to noise, and image quality metrics improvements of 4 dB increase in contrast-to-noise ratio accompanied by signal-to-noise ratio increase of 5 dB while maintaining the decrease in sharpness below 2%. In contrast, the averaging approach currently used provides 1 dB increase in contrast-to-noise ratio and signal-to-noise ratio increase of 1 dB while resulting in 70% decrease in sharpness. Performance of edge preservation or sharpness was evaluated based on correlation.
The proposed algorithm for image denoising of the retinal layers is an appropriate preprocessing approach to provide enhanced retinal OCT images for further computational analysis such as segmentation of retinal layers. Therefore, this algorithm has applications for diagnosing and monitoring patients with diabetes, hypertension, glaucoma, age related macular degeneration, and other eye diseases.
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