https://libeldoc.bsuir.by/handle/123456789/38980
Title: | Reducing of radiation dose for X-ray using contourlet transform and block thresholding technique |
Authors: | Alhamd, M. W. Salman, T. M. Melqonyan, M. |
Keywords: | материалы конференций;X-ray;block thresholding;contourlet |
Issue Date: | 2020 |
Publisher: | Беспринт |
Citation: | Alhamd, M. W. Reducing of radiation dose for X-ray using contourlet transform and block thresholding technique / M. W. Alhamd, T. M. Salman, M. Melqonyan // BIG DATA and Advanced Analytics = BIG DATA и анализ высокого уровня: сб. материалов VI Междунар. науч.-практ. конф., Минск, 20-21 мая 2020 года: в 3 ч. Ч. 1 / редкол. : В. А. Богуш [и др.]. – Минск : Бестпринт, 2020. – С. 137–146. |
Abstract: | Medical Image Denoising represents one of the fundamental challenges in the field of biological image processing and computer vision. X-Ray imaging is one of the widest used image acquisition technique in hospitals. Image denoising goal is to enhance the original X-Ray image by suppressing noise from a noise-contaminated version of the image. In this paper, a comprehensive survey of the types of noise added in X-Ray examination images was conducted, and to develop a database of five known types of X-Ray examinations in the hospital, three devices were randomly selected and 100 patients included in this survey. The survey results have been compared with the results of the International Atomic Energy Agency (IAEA) and the data have been useful to serve as a national guide or reference. Secondly, a denoising algorithm using Contourlet transform with blocking method was proposed to improve the quality of X-Ray images, which reduces the radiation dose resulting from repeated radiographic attempts to patients and workers. |
URI: | https://libeldoc.bsuir.by/handle/123456789/38980 |
ISBN: | ISBN 978-985-90533-7-5. |
Appears in Collections: | BIG DATA and Advanced Analytics = BIG DATA и анализ высокого уровня : материалы конференции (2020) |
File | Description | Size | Format | |
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Alhamd_Reducing.pdf | 1.5 MB | Adobe PDF | View/Open |
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