Speckle statistics in optical coherence tomography

Karamata, Boris; Hassler, Kai; Laubscher, Markus; Lasser, Theo

doi:10.1364/josaa.22.000593

Cited by 108 publications

(77 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is constructed from more detailed analyses of OCT speckle statistics that do not incorporate angular measurements [13]. We employ a linear systems framework, because the measured backscattered field S and the actual backscattered field G corresponding to a particular spatial volume element are related by the convolution operator ⊗: (1) The parameter x is related to the angular backscattering angle θ and the focal length of the lens f as x = f tan(θ).…”

Section: Signal-to-noise Ratio and Angular Compoundingmentioning

confidence: 99%

Angle-resolved Optical Coherence Tomography with sequential angular selectivity for speckle reduction

Desjardins¹,

Vakoc²,

Oh³

et al. 2007

Opt. Express

121

View full text Add to dashboard Cite

We present a novel method for rapidly acquiring optical coherence tomography (OCT) images at multiple backscattering angles. By angularly compounding these images, high levels of speckle reduction were achieved. Signal-to-noise ratio (SNR) improvements of 3.4 dB were obtained from a homogeneous tissue phantom, which was in good agreement with the predictions of a statistical model of speckle that incorporated the optical parameters of the imaging system. In addition, the fast acquisition rate of the system (10 kHz A-line repetition rate) allowed angular compounding to be performed in vivo without significant motion artifacts. Speckle-reduced OCT images of human dermis show greatly improved delineation of tissue microstructure.

show abstract

Section: Signal-to-noise Ratio and Angular Compoundingmentioning

confidence: 99%

Angle-resolved Optical Coherence Tomography with sequential angular selectivity for speckle reduction

Desjardins¹,

Vakoc²,

Oh³

et al. 2007

Opt. Express

121

View full text Add to dashboard Cite

show abstract

“…The visualization quality of OCT is often degraded by speckle noise as a natural consequence of limited light bandwidth, when there are multiple scatterers lying within the coherence length. The Rayleigh distribution is the first statistical model used to describe such speckle patterns [1], but it assumes perfect polarization, which in practice does not hold for real OCT systems. Recent investigations of speckle statistics consist of fitting probability distributions to the signal intensities within tissue.…”

Section: Introductionmentioning

confidence: 99%

Statistical model for OCT image denoising

Idoughi

Choudhury

et al. 2017

Biomed. Opt. Express

View full text Add to dashboard Cite

Optical coherence tomography (OCT) is a non-invasive technique with a large array of applications in clinical imaging and biological tissue visualization. However, the presence of speckle noise affects the analysis of OCT images and their diagnostic utility. In this article, we introduce a new OCT denoising algorithm. The proposed method is founded on a numerical optimization framework based on maximum-a-posteriori estimate of the noise-free OCT image. It combines a novel speckle noise model, derived from local statistics of empirical spectral domain OCT (SD-OCT) data, with a Huber variant of total variation regularization for edge preservation. The proposed approach exhibits satisfying results in terms of speckle noise reduction as well as edge preservation, at reduced computational cost.

show abstract

“…The Rayleigh model proves to be a good model for the firstorder statistics of OCT images as well [13], [14], even though under certain conditions a Gaussian model holds [15]. An analytical model that describes the performance of OCT signals in both single and multiple scattering regimes is presented in [16] and [17] and the influence of multiple scattering on the measurement of the total attenuation coefficient is described in [18] and [19].…”

Section: Speckle Noise In Octmentioning

confidence: 99%

Multiresolution Denoising for Optical Coherence Tomography: A Review and Evaluation

Pižurica¹,

Jovanov²,

Huysmans³

et al. 2008

CMIR

View full text Add to dashboard Cite

Abstract:Recently emerging non-invasive imaging modality -optical coherence tomography (OCT) -is becoming an increasingly important diagnostic tool in various medical applications. One of its main limitations is the presence of speckle noise which obscures small and low-intensity features. The use of multiresolution techniques has been recently reported by several authors with promising results. These approaches take into account the signal and noise properties in different ways. Approaches that take into account the global orientation properties of OCT images apply accordingly different level of smoothing in different orientation subbands. Other approaches take into account local signal and noise covariance's.So far it was unclear how these different approaches compare to each other and to the best available single-resolution despeckling techniques. The clinical relevance of the denoising results also remains to be determined. In this paper we review systematically recent multiresolution OCT speckle filters and we report the results of a comparative experimental study.We use 15 different OCT images extracted from five different three-dimensional volumes, and we also generate a software phantom with real OCT noise. These test images are processed with different filters and the results are evaluated both visually and in terms of different performance measures. The results indicate significant differences in the performance of the analyzed methods. Wavelet techniques perform much better than the single resolution ones and some of the wavelet methods improve remarkably the quality of OCT images.

show abstract

Speckle statistics in optical coherence tomography

Cited by 108 publications

References 11 publications

Angle-resolved Optical Coherence Tomography with sequential angular selectivity for speckle reduction

Angle-resolved Optical Coherence Tomography with sequential angular selectivity for speckle reduction

Statistical model for OCT image denoising

Multiresolution Denoising for Optical Coherence Tomography: A Review and Evaluation

Contact Info

Product

Resources

About