Motion-adaptive 3D nonlocal means filter based on stochastic distance for low-dose X-ray fluoroscopy

Min Seok Lee, Sang Wook Park, Sang Yoon Lee, Moon Gi Kang

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5 Citations (Scopus)


Low-dose X-ray fluoroscopy avoids radiation risks, but X-ray fluoroscopic image sequences are contaminated by quantum noise. In this paper, a 3D nonlocal means (NLM) filter based on stochastic distance that incorporates motion information is proposed, which can be applied to the denoising of X-ray fluoroscopic image sequences. First, the stochastic distance is obtained for use as the NLM filter similarity measure. This facilitates the removal of Poisson noise between the patches to be denoised within motion-compensated 3D volumes for spatio-temporal filtering. Second, motion state detection and motion-adaptive weights are proposed, which preserve the details of the motion that can occur during medical procedures. Experimental results obtained by applying the proposed method to real X-ray fluoroscopic image sequence images are shown in visual and numerical comparison with state-of-the-art denoising methods for spatio-temporal filtering techniques. The quantitative and qualitative results confirm that the proposed novel frameworks outperform other techniques in terms of objective criteria, as well as the subjective visual perception of the real X-ray fluoroscopic image sequences.

Original languageEnglish
Pages (from-to)74-85
Number of pages12
JournalBiomedical Signal Processing and Control
Publication statusPublished - 2017 Sep

Bibliographical note

Funding Information:
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning [grant number 2015R1A2A1A14000912]. The funding source had no role in the study design; in the collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.

Publisher Copyright:
© 2017 Elsevier Ltd

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Health Informatics


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