Techniques for Medical Images Processing Using Shearlet Transform and Color Coding : доклад, тезисы доклада

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Год издания: 2018

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

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

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 medical 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%.

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Журнал: Computer vision in control systems-4: real life applications

Выпуск журнала: Vol. 136

Номера страниц: 223-259

ISSN журнала: 18684394

Место издания: Berlin

Издатель: Springer-Verlag Berlin


  • Zotin Alexander (Reshetnev Siberian State Univ Sci & Technol, 31 Krasnoyarsky Rabochy Av, Krasnoyarsk 660037, Russia)
  • Simonov Konstantin (Russian Acad Sci, Siberian Branch, Inst Computat Modeling, 50-44 Akad Gorodok, Krasnoyarsk 660036, Russia)
  • Kapsargin Fedor (VF Voino Yasenetsky Krasnoyarsk State Med Univ, 1 Partizana Geleznyaka St, Krasnoyarsk 660022, Russia)
  • Cherepanova Tatyana (VF Voino Yasenetsky Krasnoyarsk State Med Univ, 1 Partizana Geleznyaka St, Krasnoyarsk 660022, Russia)
  • Kruglyakov Alexey (Siberian Fed Univ, 79 Svobodny Av, Krasnoyarsk 660041, Russia)
  • Cadena Luis (Univ Fuerzas Armadas ESPE, Av Gral Ruminahui S-N, Sangolqui, Ecuador)

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