Mr. Oleg Olegovich Myakinin Profile

Oleg Olegovich Myakinin

Senior Lecturer at Samara Univ

SPIE Involvement:

Author

Publications (29)

Proceedings Article | 17 November 2020 Paper

Optical biopsy of skin cancer based on Raman and fluorescence spectroscopy

Yulia Khristoforova, Ivan Bratchenko, Lyudmila Bratchenko, Oleg Myakinin, Dmitry Artemyev, Alexandr Moryatov, Sergey Kozlov, Ekaterina Borisova, Tsanislava Genova, Petranka Troyanova, Valery Zakharov

Proceedings Volume 11582, 115821K (2020) https://doi.org/10.1117/12.2581594

KEYWORDS: Raman spectroscopy, Skin, Tumors, Skin cancer, Tissues, Tissue optics, Cancer, Spectroscopy

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Proceedings Article | 17 November 2020 Paper

Possibilities for decomposing Raman spectra of amino acids mixture by Multivariate Curve Resolution (MCR) analysis

Irina Matveeva, Oleg Myakinin, Yulia Khristoforova, Ivan Bratchenko, Elena Tupikova, Valery Zakharov

Proceedings Volume 11582, 115821G (2020) https://doi.org/10.1117/12.2581559

KEYWORDS: Raman spectroscopy, Lung cancer, Signal to noise ratio, Plasma, Cancer, Analytical research, Spectroscopy, Semiconductor lasers, Proteins

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Proceedings Article | 13 April 2020 Presentation + Paper

Comparison testing of machine learning algorithms separability on Raman spectra of skin cancer

Kirill Serzhantov, Oleg Myakinin, Mariya Lisovskaya, Ivan Bratchenko, Alexander Moryatov, Sergey Kozlov, Valery Zakharov

Proceedings Volume 11359, 1135906 (2020) https://doi.org/10.1117/12.2555639

KEYWORDS: Raman spectroscopy, Skin, Machine learning, Pathology, Skin cancer, Melanoma

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The aim of this research is maximizing differentiation quality of skin neoplasms by Raman Spectroscopy (RS) and Autofluorescence (AF) using conventional Machine Learning (ML) algorithms (which means excluding Neural Networks). Thus, a basic task of this research consists of making and testing ML algorithm ensembles on Raman spectra, that were obtained in vivo in Samara Oncology Clinical Center. The data (spectra) have been obtained in a form of text files containing the identifier of a patient, as well as a Raman spectrum in the form of pair values – a wavelength and an appropriate value. All data (964 spectra) has been divided for two classes: Tumor and Skin. Further preprocessing of the input data and the analysis of models of machine learning for a problem of classification have been carried out. We used the following ML tools, namely Python 3.7.3, an open source ML libraries Scikit-learn v0.21.2, NumPy 1.17.0, and Pandas 1.0.1, IDE Anaconda Enterprise 5.3 and cloud service Google Colaboratory with an interactive environment Jupyter Notebook. Machine learning models that show a high accuracy result include Classification and Regression Tree (CART), Support Vector Classification (SVC), Logistic Regression (LR), K-nearest neighbors algorithm (KNN). These classifiers show high quality classification on standard parameters already. The Soft Voting Classification module was selected as an ensemble, that allows us to use several models of classifiers that are not similar to each other at once, combine them into one classifier. Results of this ensemble testing from these ML algorithms showed that the classification accuracy, unlike the best qualifier, has not been increased. However, the metrics of classification quality show that the model has become stabler and steady. Results: specificity - 93%, sensitivity - 88, harmonic mean between precision and recall (F1 score) - 90%. An analysis of validation and training curves indicated a small size of training data and, for some cases, a high complexity of the model, which led to a decrease in the classification accuracy.

Proceedings Article | 2 April 2020 Presentation + Paper

Additive simulation of Raman light scattering from skin cancer using the Monte Carlo method

Irina Matveeva, Oleg Myakinin, Yulia Khristoforova, Ivan Bratchenko, Alexander Moryatov, Sergey Kozlov, Valery Zakharov

Proceedings Volume 11363, 113631O (2020) https://doi.org/10.1117/12.2554904

KEYWORDS: Skin, Raman spectroscopy, Monte Carlo methods, Tissue optics, Light scattering, Raman scattering, Tissues, Skin cancer

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Proceedings Article | 2 April 2020 Presentation + Paper

Multiparametric spectral diagnosis of skin cancer

Valery Zakharov, Lyudmila Bratchenko, Yulia Khristoforova, Oleg Myakinin, Dmitry Artemyev, Alexander Moryatov, Sergey Kozlov, Ekaterina Borisova, Tsanislava Genova, Petranka Troyanova, Ivan Bratchenko

Proceedings Volume 11363, 1136310 (2020) https://doi.org/10.1117/12.2555306

KEYWORDS: Skin, Tumors, Skin cancer, Raman spectroscopy, Tumor growth modeling, Tissues, Remote sensing, Statistical analysis, In vivo imaging, Spectroscopy

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Proceedings Article | 3 September 2018 Paper

In vivo NIR Raman and autofluorescence spectroscopies of skin neoplasms

Yulia Khristoforova, Ivan Bratchenko, Oleg Myakinin, Dmitry Artemyev, Alexander Moryatov, Oleg Kaganov, Sergey Kozlov, Andrey Orlov, Valery Zakharov

Proceedings Volume 10753, 1075314 (2018) https://doi.org/10.1117/12.2320483

KEYWORDS: Skin, Raman spectroscopy, Tumors, Melanoma, Near infrared, Spectroscopy

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Proceedings Article | 24 May 2018 Presentation

Multimodal approaches for skin cancer diagnosis (Conference Presentation)

Valery Zakharov, Sergey Kozlov, Ivan Bratchenko, Oleg Myakinin, Alexander Moryatov, Andrey Orlov

Proceedings Volume 10685, 106851L (2018) https://doi.org/10.1117/12.2307206

KEYWORDS: Optical coherence tomography, In vivo imaging, Skin cancer, Diagnostics, Light emitting diodes, Hyperspectral imaging, Tissues, Ocean optics, Tissue optics, RGB color model

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A multimodal method for skin cancer diagnosis based on Dermatoscopy (DS) image analysis, Hyperspectral Imaging (HSI), Raman/Autofluorescense analysis (RS/AF) and Optical Coherence Tomography (OCT) is presented. A universal multi-class classifier for OCT is described. The diagnostic accuracy near 90% (depended from tissue type) has been demonstrated for in vivo and ex vivo cases. A multimodal unit for diagnosis of cutaneous malignant neoplasms has been proposed. It include following pure optical modalities: Raman Spectroscopy (NIR, 785 nm) in combination with Autofluorescence analysis (485 and 785 nm), Optical Coherence Tomography (840±45 nm) and Hyperspectral Imaging (visible range, 450-750 nm, 2.3 nm spectral resolution). The method is also supplemented with a Multispectral Digital Dermatoscopy, which is a perfect solution for preliminary screening and diagnosis of pathological areas by general physician. The own developed DS tool is based on the Basler acA1920-25uc camera (RGB, 12 bit/px, 1920*1080), its resolution on the surface of the skin is about 13 μm/px. The device body has been designed and printed on a 3D printer. Tissue lighting is carried out by various LEDs: UV 365 nm, White 4000 K (polarized and unpolarized LEDs); Blue 465-485 nm; Green 520-535 nm; Red 620-630 nm LEDs. The results of several in vivo and ex vivo series of experiments are presented. In vivo studies were performed in Samara Oncology Clinical Center. A total of 220 patients were examined with Malignant Melanoma (MM), Basal Cell Carcinoma (BCC) and other benign and malignant tumors. 232 tissue samples surgically resected from patients were examined ex vivo. All samples diagnoses were confirmed by histology reports after resection. A final accuracy of 97.3% was obtained using RS/AF analysis. Moreover, for case of Malignant/Benign in vivo case, it reached up to 100% with low-cost Ocean Optics QE65 Pro spectrograph and PLS analysis. A final separation of classes for OCT ex vivo leads to a high (95%) accuracy for two-classes cases and 75% for the four classes case. Combination of 2D and 3D OCT data give us an opportunity to receive common information about tumor (geometric and morphological characteristics) from data cubes and use more powerful algorithms for counting features (fractal, textural, geometric) on separated scans. These categories of features provide closer connection with ABCDE criteria (asymmetry, borders irregularity, color, diameter, evolution). Final accuracies 90% for in vivo HSI is very similar to in vivo DS accuracy, which reaches 93%. The optical density spectral feature has been used as a main diagnostic characteristic for HSI and topology texture and color analysis for RGB DS.

Proceedings Article | 24 May 2018 Presentation

In vivo fast automatic skin cancer recognition using a multispectral dermatoscopy imaging tool (Conference Presentation)

Oleg Myakinin, Oleg Мelsitov, Semyon Konovalov, Ivan Bratchenko, Alexander Moryatov, Sergey Kozlov, Valery Zakharov

Proceedings Volume 10685, 106851B (2018) https://doi.org/10.1117/12.2307243

KEYWORDS: Skin, Multispectral imaging, In vivo imaging, Melanoma, Optical filters, Skin cancer, Visible radiation, Light emitting diodes, Visualization, Ultraviolet radiation

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Malignant melanoma is the most dangerous oncological skin disease and, as a rule, rapidly progresses, spreading metastases throughout the body. In this way, it is important to diagnose this as early as possible, on early stages. However, this process requires a physician who does not always have enough time and experience to analyze a suspicious neoplasm. In this research, we have developed a device for dermatoscopic skin examination. The own developed Dermatoscope is based on the Basler acA1920-25uc camera (RGB, 12 bit/px, 1920*1080) has a resolution of ~13 μm/px on the surface of the skin. The device body has been designed and printed on a 3D printer. Tissue lighting is carried out by various LEDs: • UV: 2 x LEUVA77V20RV00 (365 nm, 1 W, 9 nm half-width); • White: 4 x LEDs FM-5630WDS-460W-R80 (39 lm, 4000 K) (two polarized LEDs and two unpolarized ones); • Blue: 2 x CREE XREBLU-L1-0000-00K01 (30 lm, 465-485 nm); • Green: 2 x CREE XRCGRN-L1-0000-00N01 (52 lm, 520-535 nm); • Red: 2 x CREE XPCReD-L1-0000-00301 (46 lm, 620-630 nm). The device takes six pictures during approximately 6 sec with different light on user’s request. Development prototype is 3D printed with ABS plastic. Used lights are white light, white polarized light for deeper skin layers visualization, UV light for fluorescence analysis, three visible colored lights for skin visualization is red, green and blue wavelength ranges which could be used for oxyhemoglobin, deoxyhemoglobin and melanin chromophore mapping. Using linear polarizing filter allows us glares filtering. Polarizing filter has been used with white light and visible colored lights in the development dermatoscope prototype. It allows us to visualize deeper skin layers and get light absorption at three visible wavelengths without glares influence. The second polarizing filter has been placed on the lens, so all of the filters placed on light sources must be oriented in one direction. Scattered excitation wavelength light must be filtered while allowing longer wavelengths to pass to get skin autofluorescence images. A 430 nm long-pass filter has been installed on the lens for this purpose. This device is also equipped with software for automatic recognition of skin melanoma based on texture (Haar wavelet texture features, local binary patterns) and color analysis. In this paper, we present the results of analysis of 225 multispectral images of melanoma and other benign and malignant tumors of the skin in vivo. The dermatoscopy tool analyzes pictures of suspicious areas, highlighting contrast areas of the skin and compares with existing diagnosed cases, which speeds up the accuracy of diagnosis. Using our software, high accuracy (96% sensitivity and 89% specificity) for melanoma has been achieved, which means a principal opportunity of effective excluding similar formations that are not melanoma. Thus, the proposed dermatosocpy tool can be used for screening procedures.

Proceedings Article | 24 May 2018 Presentation

Analysis of human skin Raman and autofluorescence as predictors of chronic diseases (Conference Presentation)

Ivan Bratchenko, Lyudmila Shamina, Dmitry Artemyev, Oleg Myakinin, Yulia Khristoforova, Valery Zakharov, Dmitriy Kornilin, Vladimir Grishanov, Peter Lebedev, Larissa Rogozina, Daria Pimenova

Proceedings Volume 10685, 106850N (2018) https://doi.org/10.1117/12.2307313

KEYWORDS: Skin, Raman spectroscopy, Visible radiation, Near infrared, Kidney, Analytical research, Endocrine system, Cardiovascular system, Biochemistry, Molecular spectroscopy

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The state of internal human homeostasis, namely the function of the internal organs - the endocrine system, the digestive tract, the nervous, hematopoietic, cardiovascular and other systems, is closely related to the skin condition. Changes in the skin biochemistry are a reflection of the internal state of the human body. Therefore, the analysis of changes in the composition of human skin various layers is one of complex parts of therapeutic disciplines. In addition to the laboratory analysis methods used today, a variety of physical methods can be successfully used to study the component composition of the human skin. Methods of Raman Spectroscopy and autofluorescence analysis can detect changes in the component composition of the skin at the molecular level. In current study we used Raman spectroscopy and autofluorescence analysis in visible and NIR regions for the analysis of human skin spectral characteristics in the presence of various influencing factors including chronic kidney transplant dysfunction. The Raman and autofluorescence spectral characteristics of studied samples in NIR region were registered using the experimental setup, incorporated a high-resolution spectrometer with integrated cooled digital camera, a fiber-optic Raman probe and the laser module with central wavelength 785 nm. The autofluorescence human skin response in visible region was registered by portable diagnostic fluorimeter, which provide an excitation light source across the 350-400 nm range and measured light intensity within the 420-600 nm range. In this study we describe the design and results of the tests on volunteers of portable fluorescence meter based on two photodiodes. One channel of such fluorometer is used for measurement of autofluorescence intensity, another one - for intensity of elastically scattered radiation, which can be used as reference. The processing of experimental data was performed on the basis of regression analysis. We performed the comparative research of Raman experimental data and visible autofluorescence analysis results. We estimated correlations between Raman and autofluorescence signals and also find informative Raman bands that may be used as predictors of general condition of the body. These bands lie in 1170 – 1700 cm-1 region. We demonstrated the possibility to measure melanin and lipofuscin levels in the skin, as they are the hallmarks of skin aging; and demonstrated the possibility to measure a level of advanced glycation end products in the skin, advanced glycation end products as and lipofuscins are markers of general body condition. In addition, we have found informative spectral bands characterizing changes in the component composition of the skin in the presence of various influencing factors as kidney diseases.

Proceedings Article | 26 April 2018 Paper

Comparative study of human blood Raman spectra and biochemical analysis of patients with cancer

Lyudmila Shamina, Ivan Bratchenko, Dmitry Artemyev, Oleg Myakinin, Alexander Moryatov, Andrey Orlov, Sergey Kozlov, Valery Zakharov

Proceedings Volume 10716, 107160D (2018) https://doi.org/10.1117/12.2315073

KEYWORDS: Raman spectroscopy, Biological research, Blood, Surface enhanced Raman spectroscopy, Chemical analysis, Pathology, Cancer, Analytical research, Tumors, Proteins

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Proceedings Article | 26 April 2018 Paper

Modeling of skin cancer dermatoscopy images

Malica Iralieva, Oleg Myakinin, Ivan Bratchenko, Valery Zakharov

Proceedings Volume 10716, 107160C (2018) https://doi.org/10.1117/12.2311953

KEYWORDS: Skin cancer, Skin, Tumors, Computer simulations, Melanoma, Cancer

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Proceedings Article | 26 April 2018 Paper

Analysis of 3D OCT images for diagnosis of skin tumors

Dmitry Raupov, Oleg Myakinin, Ivan Bratchenko, Valery Zakharov, Alexander Khramov

Proceedings Volume 10716, 1071608 (2018) https://doi.org/10.1117/12.2305405

KEYWORDS: Optical coherence tomography, Fractal analysis, Skin cancer

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Skin cancer is one of the fastest growing type of cancer. It represents the most commonly diagnosed malignancy, surpassing lung, breast, colorectal and prostate cancer. So, diagnostics for different types of skin cancer on early stages is a very high challenge for medicine industry.

New optical imaging techniques have been developed in order to improve diagnostics precision. Optical coherence tomography (OCT) is based on low-coherence interferometry to detect the intensity of backscattered infrared light from biological tissues by measuring the optical path length. OCT provides the advantage of real-time, in vivo, low-cost imaging of suspicious lesions without having to proceed directly to a tissue biopsy.

The post-processing techniques can be used for improving the precision of diagnostics and providing solutions to overcome limitations for OCT. Image processing can include noise filtration and evaluation of textural, geometric, morphological, spectral, statistic and other features. The main idea of this investigation is using information received from multiple analyze on 2D- and 3D-OCT images for skin tumors differentiating.

At first, we tested the computer algorithm on OCT data hypercubes and separated B- and C-scans. Combination of 2D and 3D data give us an opportunity to receive common information about tumor (geometric and morphological characteristics) and use more powerful algorithms for features evaluation (fractal and textural) on these separated scans. These groups of features provide closer connection to classical wide-used ABCDE criteria (Asymmetry, Border irregularity, Color, Diameter, Evolution). We used a set of features consisting of fractal dimension, Haralick's, Gabor's, Tamura's, Markov random fields, geometric features and many others.

We could note about good results on the test sets in differentiation between BCC and Nevus, MM and Healthy Skin. We received dividing MM from Healthy Skin with sensitivity more 90% and specificity more 92% (168 B-scans from 8 species) by using three Haralick’s features like Contrast, Correlation and Energy. The results are very promising to be tested for new cases and new bigger sets of OCT images.

SPIE Journal Paper | 16 February 2017 Open Access

Combined Raman and autofluorescence ex vivo diagnostics of skin cancer in near-infrared and visible regions

Ivan Bratchenko, Dmitry Artemyev, Oleg Myakinin, Yulia Khristoforova, Alexander Moryatov, Sergey Kozlov, Valery Zakharov

JBO, Vol. 22, Issue 02, 027005, (February 2017) https://doi.org/10.1117/12.10.1117/1.JBO.22.2.027005

KEYWORDS: Remote sensing, Skin, Atrial fibrillation, Near infrared, Tissues, Tumors, Raman spectroscopy, Diagnostics, Cancer, Skin cancer

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Proceedings Article | 31 October 2016 Paper

Skin cancer texture analysis of OCT images based on Haralick, fractal dimension, Markov random field features, and the complex directional field features

Dmitry Raupov, Oleg Myakinin, Ivan Bratchenko, Valery Zakharov, Alexander Khramov

Proceedings Volume 10024, 100244I (2016) https://doi.org/10.1117/12.2246446

KEYWORDS: Optical coherence tomography, Fractal analysis, Skin cancer, Tissues, Skin, Image analysis, Denoising, Tumors, Magnetorheological finishing, Melanoma

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Proceedings Article | 31 October 2016 Paper

In vivo hyperspectral imaging and differentiation of skin cancer

Larisa Zherdeva, Ivan Bratchenko, Oleg Myakinin, Alexander Moryatov, Sergey Kozlov, Valery Zakharov

Proceedings Volume 10024, 100244G (2016) https://doi.org/10.1117/12.2246433

KEYWORDS: Skin, Hyperspectral imaging, In vivo imaging, Backscatter, Tissues, Skin cancer, Cancer, Tumors, Melanoma, Principal component analysis

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Proceedings Article | 28 April 2016 Paper

Blood proteins analysis by Raman spectroscopy method

D. Artemyev, I. Bratchenko, Yu. Khristoforova, A. Lykina, O. Myakinin, T. Kuzmina, I. Davydkin, V. Zakharov

Proceedings Volume 9887, 98871Y (2016) https://doi.org/10.1117/12.2227906

KEYWORDS: Blood, Raman spectroscopy, Proteins, Plasma, Principal component analysis, Statistical analysis, Digital cameras, Biomedical optics, Medical diagnostics, Blood, Calibration, Remote sensing, Raman scattering, Semiconductor lasers, Laser scattering, Laser therapeutics

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Proceedings Article | 27 April 2016 Paper

Fluorescence spectroscopy for neoplasms control

I. Bratchenko, Yu. Kristoforova, O. Myakinin, D. Artemyev, S. Kozlov, A. Moryatov, V. Zakharov

Proceedings Volume 9887, 98870Y (2016) https://doi.org/10.1117/12.2227610

KEYWORDS: Tissues, Visible radiation, Near infrared, Spectroscopy, Melanoma, Chemical analysis, Skin, Fluorescence spectroscopy, Cancer, Atrial fibrillation, Tumors, Analytical research, Raman spectroscopy, Luminescence

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Proceedings Article | 27 April 2016 Paper

Skin cancer texture analysis of OCT images based on Haralick, fractal dimension and the complex directional field features

Dmitry Raupov, Oleg Myakinin, Ivan Bratchenko, Dmitry Kornilin, Valery Zakharov, Alexander Khramov

Proceedings Volume 9887, 98873F (2016) https://doi.org/10.1117/12.2227896

KEYWORDS: Speckle, Tissue optics, Optical coherence tomography, Tissues, Fractal analysis, Skin cancer, Melanoma, Imaging systems, Image analysis, Tumors, Skin, Denoising

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Proceedings Article | 27 April 2016 Paper

Comparison of Raman spectroscopy equipment for tissues and biofluids analysis

Yu. Khristoforova, I. Bratchenko, O. Myakinin, D. Artemyev, V. Zakharov

Proceedings Volume 9887, 98873E (2016) https://doi.org/10.1117/12.2227878

KEYWORDS: Spectrographs, Diagnostics, Skin, Raman spectroscopy, Tissues, Biological research, Principal component analysis, Spectroscopy, Skin cancer, Semiconductor lasers, Blood, Proteins, Quantum efficiency, Spectrometers, Spectroscopes, Signal detection

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Proceedings Article | 27 April 2016 Paper

Hyperspectral imaging of skin and lung cancers

Larisa Zherdeva, Ivan Bratchenko, Marina Alonova, Oleg Myakinin, Dmitry Artemyev, Alexander Moryatov, Sergey Kozlov, Valery Zakharov

Proceedings Volume 9887, 98870S (2016) https://doi.org/10.1117/12.2227602

KEYWORDS: Tissues, Hyperspectral imaging, Skin, Lung, Lung cancer, Cancer, Image registration, Skin cancer, Diagnostics, Lung cancer, Digital cameras, Melanoma, Backscatter, In vivo imaging

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Proceedings Article | 22 September 2015 Paper

Dermoscopy analysis of RGB-images based on comparative features

Oleg Myakinin, Valery Zakharov, Ivan Bratchenko, Dmitry Artemyev, Evgeny Neretin, Sergey Kozlov

Proceedings Volume 9599, 95992B (2015) https://doi.org/10.1117/12.2188165

KEYWORDS: Skin, Tumors, Melanoma, Feature extraction, Cancer, Skin cancer, Binary data, Image analysis, Diagnostics, Biological research

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Proceedings Article | 15 July 2015 Paper

Combined autofluorescence and Raman spectroscopy method for skin tumor detection in visible and near infrared regions

V. Zakharov, I. Bratchenko, D. Artemyev, O. Myakinin, Y. Khristoforova, S. Kozlov, A. Moryatov

Proceedings Volume 9537, 95372H (2015) https://doi.org/10.1117/12.2186095

KEYWORDS: Raman spectroscopy, Atrial fibrillation, Tissues, Skin, Tumors, Near infrared, Visible radiation, Remote sensing, Melanoma, Skin cancer

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Proceedings Article | 9 July 2015 Open Access

Investigation of changes in fractal dimension from layered retinal structures of healthy and diabetic eyes with optical coherence tomography

Wei Gao, Valery Zakharov, Oleg Myakinin, Ivan Bratchenko, Dmitry Artemyev, Dmitry Kornilin

Proceedings Volume 9541, 95411I (2015) https://doi.org/10.1117/12.2185222

KEYWORDS: Fractal analysis, Optical coherence tomography, Coherence imaging, Scattering media, Biomedical optics, Aerospace engineering, Current controlled current source, Contact lenses

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SPIE Journal Paper | 4 February 2015 Open Access

Comparative analysis of combined spectral and optical tomography methods for detection of skin and lung cancers

Valery Zakharov, Ivan Bratchenko, Dmitry Artemyev, Oleg Myakinin, Dmitry Kornilin, Sergey Kozlov, Alexander Moryatov

JBO, Vol. 20, Issue 02, 025003, (February 2015) https://doi.org/10.1117/12.10.1117/1.JBO.20.2.025003

KEYWORDS: Tumors, Optical coherence tomography, Remote sensing, Tissues, Skin, Raman spectroscopy, Melanoma, Cancer, Lung, Skin cancer

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Proceedings Article | 23 September 2014 Paper

The Empirical Mode Decomposition algorithm via Fast Fourier Transform

Oleg Myakinin, Valery Zakharov, Ivan Bratchenko, Dmitry Kornilin, Dmitry Artemyev, Alexander Khramov

Proceedings Volume 9217, 921721 (2014) https://doi.org/10.1117/12.2061808

KEYWORDS: Fourier transforms, Optical coherence tomography, Signal processing, Nonlinear optics, Wavelet transforms, Analog electronics, Denoising, Signal analysis, Discrete wavelet transforms, Time-frequency analysis

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Proceedings Article | 5 September 2014 Paper

Combined Raman spectroscopy and autofluoresence imaging method for in vivo skin tumor diagnosis

V. Zakharov, I. Bratchenko, O. Myakinin, D. Artemyev, Y. Khristoforova, S. Kozlov, A. Moryatov

Proceedings Volume 9198, 919804 (2014) https://doi.org/10.1117/12.2061667

KEYWORDS: Skin, Tumors, Raman spectroscopy, Atrial fibrillation, In vivo imaging, Tissues, Melanoma, Remote sensing, Imaging spectroscopy, Skin cancer

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Proceedings Article | 8 May 2014 Paper

A complex noise reduction method for improving visualization of SD-OCT skin biomedical images

Oleg Myakinin, Valery Zakharov, Ivan Bratchenko, Dmitry Kornilin, Alexander Khramov

Proceedings Volume 9129, 91292Y (2014) https://doi.org/10.1117/12.2051977

KEYWORDS: Optical coherence tomography, Denoising, Visualization, Signal to noise ratio, Skin, Biomedical optics, Linear filtering, CCD cameras, Tissues, Charge-coupled devices

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