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Neural Denoising of Ultra-low Dose Mammography

  • Michael GreenEmail author
  • Miri Sklair-Levy
  • Nahum Kiryati
  • Eli Konen
  • Arnaldo Mayer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11905)

Abstract

X-ray mammography is commonly used for breast cancer screening. Radiation exposure during mammography restricts the screening frequency and minimal age. Reduction of radiation dose decreases image quality. Image denoising has been recently considered as a way to facilitate dose reduction in mammography without impacting its diagnostic value. We propose a convolutional locally-consistent non-local means (CLC-NLM) algorithm for ultra-low dose mammography denoising. The proposed method achieves powerful denoising while preserving fine details in high resolution mammography. Validation is performed using a dataset of 16 digital mammography cases (4-views each). Since obtaining true low-dose and high-dose mammogram pairs raises regulatory concerns, we applied the X-ray specific and validated method of Veldkamp et al. to simulate 90% dose reduction. The proposed algorithm is shown to compete favorably, both quantitatively and qualitatively, against state-of-the-art neural denoising algorithms. In particular, tiny micro-calcifications are better preserved using the proposed algorithm.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Michael Green
    • 1
    Email author
  • Miri Sklair-Levy
    • 2
  • Nahum Kiryati
    • 3
  • Eli Konen
    • 2
  • Arnaldo Mayer
    • 2
  1. 1.School of Electrical EngineeringTel-Aviv UniversityTel Aviv-YafoIsrael
  2. 2.Diagnostic Imaging, Sheba Medical Center, Affiliated to the Sackler School of MedicineTel-Aviv UniversityTel Aviv-YafoIsrael
  3. 3.The Manuel and Raquel Klachky Chair of Image Processing, School of Electrical EngineeringTel-Aviv UniversityTel Aviv-YafoIsrael

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