Wavelet-based denoising for 3D OCT images

Abstract

Optical coherence tomography produces high resolution medical images based on spatial and temporal coherence of the optical waves backscattered from the scanned tissue. However, the same coherence introduces speckle noise as well; this degrades the quality of acquired images.In this paper we propose a technique for noise reduction of 3D OCT images, where the 3D volume is considered as a sequence of 2D images, i.e., 2D slices in depth-lateral projection plane. In the proposed method we first perform recursive temporal filtering through the estimated motion trajectory between the 2D slices using noise-robust motion estimation/compensation scheme previously proposed for video denoising. The temporal filtering scheme reduces the noise level and adapts the motion compensation on it. Subsequently, we apply a spatial filter for speckle reduction in order to remove the remainder of noise in the 2D slices. In this scheme the spatial (2D) speckle-nature of noise in OCT is modeled and used for spatially adaptive denoising. Both the temporal and the spatial filter are wavelet-based techniques, where for the temporal filter two resolution scales are used and for the spatial one four resolution scales.The evaluation of the proposed denoising approach is done on demodulated 3D OCT images on different sources and of different resolution. For optimizing the parameters for best denoising performance fantom OCT images were used. The denoising performance of the proposed method was measured in terms of SNR, edge sharpness preservation and contrast-to-noise ratio. A comparison was made to the state-of-theart methods for noise reduction in 2D OCT images, where the proposed approach showed to be advantageous in terms of both objective and subjective quality measures.

Statistical Estimation of Ballistic Signal in Visible Light OCT Based on Random Matrix Description

“…It should be noted that RMT reconstruction was not proposed an denoising method to substite the current methods like wavelet-based denoising [15,16], anisotropy curvelet transform [17], and sparsity-based denoising [18], but as a pre-processing for these existing denoise methods. Fig.…”

Denoising based on noise parameter estimation in speckled OCT images using neural network

“…Many methods have been proposed to reduce noise in OCT. They include hardware modifications of the OCT system design [2] and application of image processing like locally adaptive filtering [3,4], soft thresholding of the wavelet subbands with various configurations [3,5,6], and averaging in time and frequency domain [7].…”