Techniques for medical images processing using shearlet transform and color coding


Год издания: 2018

Идентификатор DOI: 10.1007/978-3-319-67994-5_9

Ключевые слова: 2D cleaner filter, Edge detection, Gaussian filter, Mean filter, Median filter, Medical image processing, Parallel programming, Shearlet transform

Аннотация: Image processing techniques play an important role in the diagnostics and detection of diseases and monitoring the patients having these diseases. The chapter presents the medical image processing and morphological analysis in the solution of urology and plastic surgery (hernioplasty) problems. Novel methodology for processing mediПоказать полностьюcal images using a color coding of contour representation obtained by Digital Shearlet Transform (DST) has been presented. The object contours in the medical urology images are obtained using the conventional filters, and then results are compared. Since medical images can contain some noise, it makes sense to suppress the noise at the preprocessing step. For this purpose, the optimized in implementation algorithms of the most frequently used filters, such as the mean filter, Gaussian filter, median filter, and 2D cleaner filter, had been developed. A comparison of the optimized and ordinary implementations of noise reduction filter shows great speed improvement of the optimized implementations (around 3–20 times). Additionally, the parallel implementation gives 2–3.5 times performance boost. The proposed methodology allows to improve the accuracy and decrease the error of the sought parameters and characteristics by 10–20% on average without a lack of significant details in the structural features of the examined objects. The results of the experimental study show an error decrease in data representation for the plastic surgery (hernioplasty) by 15–25%. © 2018, Springer International Publishing AG.

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Журнал: Intelligent Systems Reference Library

Выпуск журнала: Vol. 136

Номера страниц: 223-259

ISSN журнала: 18684394

Издатель: Springer Science and Business Media Deutschland GmbH


  • Zotin A. (Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy av., Krasnoyarsk, Russian Federation)
  • Simonov K. (Institute of Computational Modeling of the Siberian Branch of the Russian Academy of the Sciences, 50/44 Akademgorodok, Krasnoyarsk, Russian Federation)
  • Kapsargin F. (V.F. Voino-Yasenetsky Krasnoyarsk State Medical University, 1 Partizana Geleznyaka St., Krasnoyarsk, Russian Federation)
  • Cherepanova T. (V.F. Voino-Yasenetsky Krasnoyarsk State Medical University, 1 Partizana Geleznyaka St., Krasnoyarsk, Russian Federation)
  • Kruglyakov A. (Siberian Federal University, 79 Svobodny av., Krasnoyarsk, Russian Federation)
  • Cadena L. (Universidad de las Fuerzas Armadas ESPE, Av. Gral Ruminahui s/n, Sangolqui, Ecuador)

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