Enhanced optical coherence tomography imaging using a histogram-based denoising algorithm

Kim, Keo-Sik; Park, Hyoung-Jun; Kang, Hyun Seo

doi:10.1117/1.oe.54.11.113110

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…Существуют различные методы фильтрации шумов рассеяния в ОКТ-сигналах [22][23][24][25][26][27][28][29][30][31]. Среди них широкое распространение получили методы накопления сигналов, выделения полезной составляющей сигнала с помощью априорной оценки параметров и модели шума, цифровой фильтрации (медианной, винеровской, билатеральной и пороговой), а также вейвлетной фильтрации.…”

Section: Introductionunclassified

Экспериментально Обучаемый Метод Фильтрации Шумов Рассеяния В Сигналах Оптической Когерентной Томографии-=sup=-*-=/Sup=-

Долганова¹,

Черномырдин²,

Александрова³

et al. 2019

Журнал Технической Физики

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A method for wavelet filtration procedure training for optical coherence tomography (OCT) images using the experimental measurements of test objects that were constructed by means of water solutions of monodisperse nanoparticles and several microscopic inclusions has been described in the present paper. The choice of test-object parameters (concentration of water solution, size of nanoparticles, and shape, dimensions, and mutual position of inclusions) has allowed the modeling of various working conditions of OCT and setting different criteria for estimation of filtration efficiency. In the present work, the optimal filter for the considered example of a test object has been selected among the combinations of various basic functions of five wavelet families, soft and hard threshold filtering methods, four decomposition levels, and threshold values in a range of 0.05–3.05. The mutual position of the micro-inclusions has been used as a criterion for evaluating the filtration efficiency. As a result, it has been shown that the determined wavelet filter leads to effective suppression of the scattering noise in OCT images and preserve information about the structure of the object under study.

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Section: Introductionunclassified

Экспериментально Обучаемый Метод Фильтрации Шумов Рассеяния В Сигналах Оптической Когерентной Томографии-=sup=-*-=/Sup=-

Долганова¹,

Черномырдин²,

Александрова³

et al. 2019

Журнал Технической Физики

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“…Digital filtering as an effective method of noise reduction has been recently implemented in various ways. 9,10,[45][46][47][48][49][50][51] Since it requires a single OCT image, it can be applied for in vivo and in vitro biomedical imaging. Median filtration is a common, simple, and quite effective method.…”

Section: Introductionmentioning

confidence: 99%

“…9,47,48 The histogram-based threshold filters show better filtration quality but require knowledge of a priori information about the object. 46 For objects with repeatedly appearing features, it is possible to use a nonlocal mean filter with double Gaussian anisotropic kernels, 10 which yields better results in comparison to Wiener, median, and bilateral filters. Effectively used in image formation and processing, wavelet analysis was applied in OCT for noise reduction.…”

Section: Introductionmentioning

confidence: 99%

Nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography

et al. 2018

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We present the nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography (OCT). It employs an experimental training algorithm based on imaging of a test-object, made of the colloidal suspension of the monodisperse nanoparticles and contains the microscale inclusions. The geometry and the scattering properties of the test-object are known a priori allowing us to set the criteria for the training algorithm. Using a wide set of the wavelet kernels and the wavelet-domain filtration approaches, the appropriate filter is constructed based on the test-object imaging. We apply the proposed approach and chose an efficient wavelet denoising procedure by considering the combinations of the decomposition basis from five wavelet families with eight types of the filtration threshold. We demonstrate applicability of the wavelet-filtering for the in vitro OCT image of human brain meningioma. The observed results prove high efficiency of the proposed OCT image denoising technique.

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“…One of the noises comes from autocorrelation noise. This type of noise has been extensively studied in prior works [11–13]. Among the methods, minimum variance mean line subtraction can effectively reduce this fixed-pattern noise [12].…”

Section: Introductionmentioning

confidence: 99%

Ghost Reduction in CP-SSOCT Having Multiple References Using Fourier-Domain Shift and Sum

Cheon

Gehlbach²,

Kang

2016

IEEE Photon. Technol. Lett.

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We propose, test and validate a novel Fourier-domain based method for ghost image artifacts reduction in a common-path SSOCT system having multiple adjacent reference planes. Common-path probes with imaging systems containing high-index sapphire ball or other lenses produce multiple fixed references due to Fresnel reflections from the lens surfaces. The multiple reference planes produce multiple and overlapping OCT images. Since such ghost artifacts are the result of the superposition of multiple identical images having different amplitudes and spatial shifts, one can correctly shift and sum the images in the Fourier-domain once the relative amplitude and lateral position between the reference planes are known. This theory and numerical testing are presented to elucidate our method. We then validate the potential effectiveness using OCT imaging experiments.

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Enhanced optical coherence tomography imaging using a histogram-based denoising algorithm

Cited by 6 publications

References 20 publications

Экспериментально Обучаемый Метод Фильтрации Шумов Рассеяния В Сигналах Оптической Когерентной Томографии-=sup=-*-=/Sup=-

Экспериментально Обучаемый Метод Фильтрации Шумов Рассеяния В Сигналах Оптической Когерентной Томографии-=sup=-*-=/Sup=-

Nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography

Ghost Reduction in CP-SSOCT Having Multiple References Using Fourier-Domain Shift and Sum

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