Сибирский федеральный университет

Тип публикации: научное издание

Год издания: 2020

Идентификатор DOI: 10.1007/978-3-030-33795-7_9

Ключевые слова: elastic maps, image processing, median filter, medical image, noise reduction, retinex, shearlet transform, wavelet transform

Аннотация: The chapter is devoted to computational methods for evaluation the indicators of the tissue regeneration process using as an example the medical data of mesh nickelide titanium implants obtained during clinical experiment. Processing and analysis of scanning electron microscopy and classical histological data are performed using a set of algorithms and their modifications, which allows simplify the data analysis procedure and improve the accuracy of estimates (15–20%). The proposed technique as a computational toolkit for analyzing the dynamics of the process under study, as well as. For highlighting the internal geometric features of the experimental images of objects of interest contains algorithms of shearlet and wavelet transforms and the algorithms for elastic maps generation with color coding, which allows to obtain more representative visualization of spatial data. An important aspect of the proposed methodology is a use of brightness correction by algorithm based on Retinex technology. It allows to obtain unified average brightness of analyzed images and, in some cases, increase local contrast, as a result it affects the quality of application of the computer-based evaluation tools offered in the work. Thus, the estimation errors are reduced by 1–5% in compared to processing without brightness correction. © Springer Nature Switzerland AG 2020.

Журнал: Intelligent Systems Reference Library

Выпуск журнала: Vol. 175

Номера страниц: 265-294

ISSN журнала: 18684394

Издатель: Springer Science and Business Media Deutschland GmbH

• Zotin A. (Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochу pr., Krasnoyarsk, 660037, Russian Federation)
• Simonov K. (Institute of Computational Modelling of the Siberian Branch of the Russian Academy of Sciences, 50/44 Akademgorodok, Krasnoyarsk, 660036, Russian Federation)
• Kapsargin F. (V.F. Voino-Yasenetsky Krasnoyarsk State Medical University, 1 Partizana Geleznyaka st., Krasnoyarsk, 660022, Russian Federation)
• Cherepanova T. (V.F. Voino-Yasenetsky Krasnoyarsk State Medical University, 1 Partizana Geleznyaka st., Krasnoyarsk, 660022, Russian Federation)
• Kruglyakov A. (Siberian Federal University, 79 Svobodny pr., Krasnoyarsk, 660041, Russian Federation)

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