A new algorithm for speckle reduction of optical coherence tomography images

Avanaki, Kamran; Marques, Manuel J.; Bradu, Adrian; Hojjatoleslami, Ali; Podoleanu, Adrian Gh.

doi:10.1117/12.2041943

Cited by 8 publications

(4 citation statements)

References 17 publications

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“…47,60,65,66 In addition to these 3 main categories, there are artificial neural networkbased denoising methods that are considered as effective speckle reduction approaches. [67][68][69][70][71][72] Intensity decay is due to decline in the incident and backscattered light amplitudes when it passes through a biological sample. 1 The decay follows the Beer-Lambert law in the simplest model of skin-a single scattering model.…”

Section: Biomedical Engineering and Computational Biologymentioning

confidence: 99%

“…47,60,65,66 In addition to these 3 main categories, there are artificial neural network–based denoising methods that are considered as effective speckle reduction approaches. 67–72…”

mentioning

confidence: 99%

“…Optical coherence tomography images of a 24-year-old male’s thumb pad. (A) Original image, (B) despeckled image, 51,57,64,66,67,72,87 (C) deconvolved image, 80,86 and (D) attenuation-compensated image. 88 Yellow arrows refer to the clearer structures at deeper depths before and after attenuation compensation.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Optical Coherence Tomography Technology and Quality Improvement Methods for Optical Coherence Tomography Images of Skin: A Short Review

Adabi

Turani

Fatemizadeh

et al. 2017

Biomed Eng Comput Biol

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Optical coherence tomography (OCT) delivers 3-dimensional images of tissue microstructures. Although OCT imaging offers a promising high-resolution method, OCT images experience some artifacts that lead to misapprehension of tissue structures. Speckle, intensity decay, and blurring are 3 major artifacts in OCT images. Speckle is due to the low coherent light source used in the configuration of OCT. Intensity decay is a deterioration of light with respect to depth, and blurring is the consequence of deficiencies of optical components. In this short review, we summarize some of the image enhancement algorithms for OCT images which address the abovementioned artifacts.

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Section: Biomedical Engineering and Computational Biologymentioning

confidence: 99%

“…47,60,65,66 In addition to these 3 main categories, there are artificial neural network–based denoising methods that are considered as effective speckle reduction approaches. 67–72…”

mentioning

confidence: 99%

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Optical Coherence Tomography Technology and Quality Improvement Methods for Optical Coherence Tomography Images of Skin: A Short Review

Adabi

Turani

Fatemizadeh

et al. 2017

Biomed Eng Comput Biol

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“…The speckle attributes are affected by the scale representation, the optical settings, and biological tissue scattering characteristics [31]. Many groups including ours has attempted to despeckle OCT images [6,[32][33][34][35][36][37][38][39][40] with frame-averaging approaches [32,35] being the most widely accepted method. In this approach, multiple B-scans acquired from the same location are registered and averaged, resulting in improved contrast [32,35] and luminance [41].…”

Section: Introductionmentioning

confidence: 99%

A fast normalization and de-speckled method for skin optical coherence tomography image via deep learning

Rahaman

Lukas

May

et al. 2023

Photonics in Dermatology and Plastic Surgery 2023

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Optical coherence tomography (OCT) is well-known for its high-resolution, non-invasive imaging modality with many medical uses, including skin imaging. Nevertheless, speckle noise limits the analytical capabilities of this imaging tool, causing deterioration in contrast and less exact detection of tissue microstructural heterogeneity. To address this issue, we proposed OCT despeckling approach by combing it with normalization to reduce the speckle noise more effectively. The proposed method contains multiple steps including phase correlation for alignment of misaligned frames, frame averaging which minimizes speckle noise, region-wise pixels normalization that helps to normalize intensity pixels, a modified BM3D filtering to suppress the white and speckle, and contrast enhancement to improve the contrast appropriately. To establish the approach, we applied 130 distinct B-scan skin OCT images and validate and evaluate the performance using qualitatively and quantitatively. Although the output obtained by the algorithm is promising, the method is time-consuming because of a series of steps. To reduce the time complexity, we also develop a supervised deep learning model by mapping between noisy-despeckled image pairs. The effectiveness and applicability of our DL approach were assessed using 130 skin OCT B-scans from various body areas taken from 45 healthy people between the ages of 20 and 60. With the support of the experimental results, we demonstrate that our DL model is capable to normalize and despeckling OCT images simultaneously.

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