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A Novel DWTTH Approach for Denoising X-Ray Images Acquired Using Flat Detector

  • Olfa Marrakchi CharfiEmail author
  • Naouel Guezmir
  • Jérôme Mbainaibeye
  • Mokhtar Mars
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 146)

Abstract

This paper proposes a new approach for denoising an X-ray flat detector image by combining Discrete Wavelets Transform (DWT) and the hard Thresholding method (DWTTH). The developed procedure can decrease noise for X-ray images to achieve a great quality of image at minimum X-ray dose. Noisy images are those acquired with low X-ray doses. For this purpose we have tested our DWTTH algorithm on one low X-ray dose image (Low_RX). The denoised image is compared to a standard X-ray dose image (S_RX). Images are acquired on a Pro-Digi phantom.

We have focused our study on denoising the image with preserving contrast between regions. So, denoising procedure is applied on seven region of interest (ROI) selected on the two Pro-Digi X-ray images with different contrast.

The proposed denoising DWTTH method is based on the combination of discrete wavelet transform and hard thresholding of energy coefficients of the approximation image issued from DWT applied on several decomposition levels. The denoised image is reconstructed by applying the inverse DWT.

The DWTTH results are evaluated in terms of Contrast to Noise Ratio (CNR) and the Signal to Noise Ratio (SNR). These ratios are computed for each denoised ROI and are compared to those corresponding ROIs of S_RX image. The DWTTH method results show that the SNR and the CNR ratios are improved considerably compared to those obtained by the Wavelet Coefficient Magnitude Sum (WCMS), the soft thresholding and the conventional filtering methods.

Keywords

X-ray image Pro-Digi phantom Flat detector Image denoising Wavelet transform Thresholding 

Notes

Acknowledgment

Authors acknowledge are addressed to the Charles Nicolle Hospital radiology staff for the data base acquisitions.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Olfa Marrakchi Charfi
    • 1
    • 2
    Email author
  • Naouel Guezmir
    • 1
    • 3
  • Jérôme Mbainaibeye
    • 4
  • Mokhtar Mars
    • 5
  1. 1.Department of Physic and Instrumentation, National High Institute of Applied Sciences and TechnologyCarthage UniversityTunisTunisia
  2. 2.GREEN-TEAM Laboratory LR17AGR01 INATTunisTunisia
  3. 3.MMA Laboratory IPESTTunisTunisia
  4. 4.University of DobaDobaChad
  5. 5.Laboratory of Biophysics Research and Medicals TechnologiesHigh Institute of Tunisian MedicalsTunisTunisia

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