A noise statistical distribution analysis-based two-step filtering mechanism for optical coherence tomography image despeckling

Optical coherence tomography (OCT) inevitably suffers from speckle noises that originate from the coherent multiple-scattered photons. Such speckle noises, following different distribution patterns, can hide tissue microstructures and degrade the disease diagnosis accuracy. So far, various schemes have been proposed for despeckling in OCT images, yet few have evaluated the impacts of different noise patterns on despeckling effects. In this study, we evaluate the influences of statistical noise distributions on OCT despeckling and propose a noise distribution analysis-based despeckling method for OCT images. Specifically, we establish a noise model by dividing speckle noises into multiplicative and additive ones first, and then propose a two-step filtering mechanism, namely, augmented Lagrange function minimization and Rayleigh alpha-trimmed filtering (AR) method, to suppress such noises separately while maintain tissue microstructures. Simulations with both synthetic and practical OCT images are conducted to verify the effectiveness of the proposed AR method. Results show that the influence of multiplicative noises on OCT images are more significant, and the AR method is capable of suppressing both multiplicative and additive noises effectively, e.g. it improves the peak signal-to-noise ratio and structural similarity index measurements by 83.22% and 812.88% for typical retinal images, respectively, while retaining the important image structural details with less computational time.

Automated summary

In this study, the influences of statistical noise distributions on OCT despeckling were evaluated, and a noise distribution analysis-based despeckling method was proposed. By establishing a noise model and using a two-step filtering mechanism, the proposed method is capable of effectively suppressing both multiplicative and additive noises while retaining important image structural details.