Wavelet denoising of multiframe optical coherence tomography data

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.

Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images

“…B-scan averaging or other special scanning patterns [35,36] reduces noise but decreases the image acquisition speed. Thus, to improve the quality of our captured images, we denoise individual B-scans in the SD-OCT volume using the sparsity-based block matching and 3D-filtering (BM3D) denoising method that is freely available online [37].…”

Statistical model for OCT image denoising

“…Apart from hardware modifications, there are also algorithmic approaches. Mayer et al [8] introduce a wavelet-based method that utilizes 2D wavelet decomposition of single frames and re-weights the wavelet coefficients for reconstruction. This method requires acquisition of multiple frames throughout the whole investigation region, therefore leading to slower scanning speed.…”

Automatic segmentation of up to ten layer boundaries in SD-OCT images of the mouse retina with and without missing layers due to pathology

“…Using B-scan averaging or other special scanning patterns [29,30] reduces noise but decreases the image acquisition speed. Thus, to improve the quality of our captured images, we denoise individual B-scans in the SD-OCT volume using two different sparsity based denoising methods, which are freely available online.…”