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

Desjardins, Adrien E.; Vakoc, Benjamin J.; Oh, Wang‐Yuhl; Motaghiannezam, S. M. R.; Tearney, Guillermo J.; Bouma, Brett E.

doi:10.1364/oe.15.006200

Cited by 121 publications

(84 citation statements)

References 14 publications

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“…According to the number of frames involved, OCT denoising methods can be grouped into two categories: single-frame denoising and multi-frame denoising. The most common multi-frame technique is to acquire uncorrelated OCT images of the same spot at multiple backscattering angles [5], or at different spatial positions [6]. An average is taken over all uncorrelated frames to improve the signal-tonoise ratio (SNR).…”

Section: Introductionmentioning

confidence: 99%

Statistical model for OCT image denoising

Idoughi

Choudhury

et al. 2017

Biomed. Opt. Express

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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.

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Section: Introductionmentioning

confidence: 99%

Statistical model for OCT image denoising

Idoughi

Choudhury

et al. 2017

Biomed. Opt. Express

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“…In the context of this paper, it may be considered as a form of noise that compromises the ability of automated methods to identify structural features and boundaries in OCT images. Many hardware-based methods [22][23][24][25][26], and software-based methods [27,28] have been proposed to perform noise reduction whilst minimizing the effect on image sharpness.…”

Section: Noise Reductionmentioning

confidence: 99%

Automated quantification of lung structures from optical coherence tomography images

Pagnozzi

Kirk

Kennedy

et al. 2013

Biomed. Opt. Express

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Abstract:Characterization of the size of lung structures can aid in the assessment of a range of respiratory diseases. In this paper, we present a fully automated segmentation and quantification algorithm for the delineation of large numbers of lung structures in optical coherence tomography images, and the characterization of their size using the stereological measure of median chord length. We demonstrate this algorithm on scans acquired with OCT needle probes in fresh, ex vivo tissues from two healthy animal models: pig and rat. Automatically computed estimates of lung structure size were validated against manual measures. In addition, we present 3D visualizations of the lung structures using the segmentation calculated for each data set. This method has the potential to provide an in vivo indicator of structural remodeling caused by a range of respiratory diseases, including chronic obstructive pulmonary disease and pulmonary fibrosis. References and links1. G. Turato, R. Zuin, and M. Saetta, "Pathogenesis and pathology of COPD," Respiration 68(2), 117-128 (2001). 2. T. C. Kravis, A. Ahmed, T. E. Brown, J. D. Fulmer, and R. G. Crystal, "Pathogenic mechanisms in pulmonary fibrosis: collagen-induced migration inhibition factor production and cytotoxicity mediated by lymphocytes," J. Clin. Invest. 58(5), 1223-1232 (1976

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“…[8][9][10][11] Many methods have been proposed to attenuate the speckle noise in OCT images, these methods can be divided into two parts: hardware and software methods. Hardware methods are optical approaches that physically remove speckle noise, including frequency 12,13 and spatial compounding, [14][15][16][17][18] which can signi¯cantly improve signal-to-noise ratio of OCT images. But these methods would increase system complexity, meanwhile decrease imaging speed and spatial resolution.…”

Section: Introductionmentioning

confidence: 99%

Improved wavelet hierarchical threshold filter method for optical coherence tomography image de-noising

Cao

Wang

et al. 2018

J. Innov. Opt. Health Sci.

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According to the speckle feature in Optical coherence tomography (OCT), images with speckle indicate not only noise but also signals, an improved wavelet hierarchical threshold¯lter (IWHTF) method is proposed. At¯rst, a modi¯ed hierarchical threshold-selected algorithm is used to prevent signals from being removed by assessing suitable thresholds for di®erent noise levels. Then, an improved wavelet threshold function based on two traditional threshold functions is proposed to trade-o® between speckle removing and sharpness degradation. The de-noising results of an OCT¯nger skin image shows that the IWHTF method obtains better objective evaluation metrics and visual image quality improvement. When ¼ 0:2, ¼ 5:0 and K ¼ 1:2, the improved method can achieve 9.58 dB improvement in signal-to-noise ratio, with sharpness degraded by 3.81%.

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

Cited by 121 publications

References 14 publications

Statistical model for OCT image denoising

Statistical model for OCT image denoising

Automated quantification of lung structures from optical coherence tomography images

Improved wavelet hierarchical threshold filter method for optical coherence tomography image de-noising

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