Mr. Dmitry S. Raupov Profile

Dmitry S. Raupov

at Samara Univ

SPIE Involvement:

Author

Publications (3)

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

Read Abstract +

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.

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

Read Abstract +

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

Read Abstract +

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Keywords/Phrases

Search In:

Publication Years