Speckle noise reduction and motion artifact correction based on modified statistical parameters estimation in OCT images

Abstract

Reducing coherent noise is particularly important in interferometry-based applications, especially optical coherence tomography where more information about images can be extracted by statistical denoising and jitter refinement methods. In this paper, we not only investigate two types of medical image quality degradation factors including speckle noise and motion artifacts, but also describe the relation between them. We applied the concept of using modified approach based on statistical methods for estimation the multiplicative speckle noise parameters and utilizing the modified Minimum Mean-Squared Error noise-free estimation. After the noise reduction, the method reconstructs images based on estimated values calculated from particles jitter and displacements. This algorithm helps us to correct the location of layers to increase the quality of consecutive imaging using multiple-image input filters. Simulation results demonstrate that this method is capable of denoising when compared to the others in terms of signal-to-noise, mean squared error, and edge preservation (η) measures, and has the best results of motion artifact correction error in comparison to the classic methods.

Fast motion correction in optical coherence tomography with image feature matching

“…Even when the subject remains stationary, unconscious movements in tissue can still occur due to factors like respiration and heartbeat 4 . These involuntary movements will induce motion in the cross-section along the slow scan direction that manifests as distortion in the axial and lateral directions 5 , thereby significantly degrading the image quality.…”

Retinal cross‐section motion correction in three‐dimensional retinal optical coherence tomography

“…However, generated by a two‐dimensional (2D) grid of A‐scans, a volume demands several seconds for acquisition, while a cross‐sectional image (B‐scan) requires microseconds. Involuntary movements, such as heartbeat and respiration, will induce motion in the cross‐section along the y‐direction (slow‐scan direction) that manifests as distortion in the axial and lateral directions [9, 10].…”

Sparse PDE for SAR image speckle suppression

“…Although optical images and SAR images have different scattering characters, many denoising methods for optical images have been extended to speckle suppression. These methods include wavelet method [5, 6], variational method [7–10], partial differential equation (PDE) method [11–15], statistical method [16–19]. Different approaches have different starting points.…”