Multi-Dimensional Denoising of Real-Time Oct Imaging Data

Ralston, Tylerâ S.; Atkinson, Ian C.; Kamalabadi, F.; Boppart, Stephen A.

doi:10.1109/icassp.2006.1660551

Cited by 6 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Multiple manuscripts in the literature have now specifically used noise distributions for OCT denoising [41], [42]. Both Ralston et al [41] and Yin et al [42] derive OCT denoising methods by modeling the noise as Gaussian in the log domain. However, the Gaussian distribution has not been empirically and statistically validated in the retina.…”

Section: Introductionmentioning

confidence: 99%

Statistical Models of Signal and Noise and Fundamental Limits of Segmentation Accuracy in Retinal Optical Coherence Tomography

DuBose

Cunefare

Cole

et al. 2018

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

Optical coherence tomography (OCT) has revolutionized diagnosis and prognosis of ophthalmic diseases by visualization and measurement of retinal layers. To speed up the quantitative analysis of disease biomarkers, an increasing number of automatic segmentation algorithms have been proposed to estimate the boundary locations of retinal layers. While the performance of these algorithms has significantly improved in recent years, a critical question to ask is how far we are from a theoretical limit to OCT segmentation performance. In this paper, we present the Cramèr-Rao lower bounds (CRLBs) for the problem of OCT layer segmentation. In deriving the CRLBs, we address the important problem of defining statistical models that best represent the intensity distribution in each layer of the retina. Additionally, we calculate the bounds under an optimal affine bias, reflecting the use of prior knowledge in many segmentation algorithms. Experiments using in vivo images of human retina from a commercial spectral domain OCT system are presented, showing potential for improvement of automated segmentation accuracy. Our general mathematical model can be easily adapted for virtually any OCT system. Furthermore, the statistical models of signal and noise developed in this paper can be utilized for the future improvements of OCT image denoising, reconstruction, and many other applications.

show abstract

Section: Introductionmentioning

confidence: 99%

Statistical Models of Signal and Noise and Fundamental Limits of Segmentation Accuracy in Retinal Optical Coherence Tomography

DuBose

Cunefare

Cole

et al. 2018

IEEE Trans. Med. Imaging

View full text Add to dashboard Cite

show abstract

“…The logarithm of the magnitude is processed because speckle can be modeled as multiplicative noise to the image, which becomes additive noise once the logarithm is taken. Additionally, a multidimensional wavelet method utilizes correlations in time-series data to suppress noise via decorrelation [7]. An example of denoising using this technique is shown in Fig.…”

Section: Speckle Reduction and Signal Improvementmentioning

confidence: 99%

Data Analysis and Signal Postprocessing for Optical Coherence Tomography

Ralston¹,

Marks²,

Ahmad³

et al. 2015

Optical Coherence Tomography

View full text Add to dashboard Cite

“…The Gaussian assumption has been shown to be sufficiently accurate for most practical applications. Based on this assumption, Ralston et al [17] showed successful denoising results on experimental data. Also, in practice, the noise floor is about 40 dB below the signal level (small-noise regime) [15], [18], [19]; i.e.,…”

Section: Bias and Variance Calculationsmentioning

confidence: 99%