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Proceedings Paper

Contextual filtering in curvelet domain for fluoroscopic sequences
Author(s): Carole Amiot; Jérémie Pescatore; Jocelyn Chanussot; Michel Desvignes
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Paper Abstract

X-ray exposure during image guided interventions can be important for the patient as well as for the medical staff. Therefore dose reduction is a major concern. Nevertheless, decreasing the dose per image affects significantly the image quality. As a matter of fact, this tends to increase the noise and reduce the contrast. Hence, we propose a new and efficient method to reduce the noise in low dose fluoroscopic sequences. Many studies in that domain have been proposed implementing either multi-scale approaches using wavelet with its derivatives or using filters in the direct space. Our work is based on a spatio-temporal denoising filter using the curvelet transform. Indeed, this sparse transform represents well smooth images with edges and can be applied to fluoroscopic images in order to achieve robust denoising performances. Therefore, we propose to combine a temporal recursive filter with a spatial curvelet filter. Our work is focused on the use of the statistical dependencies between the curvelet coefficients in order to optimize the threshold function. Determining the correlation among coefficients allows to detect which coefficients represent the relevant signal. Thus, our method allows to diminish or even to erase curvelet-like artefacts. The performances and robustness of the proposed method are assessed both on synthetic and real low dose sequences (ie: 20 nGy/frame).

Paper Details

Date Published: 13 March 2013
PDF: 6 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 86690N (13 March 2013); doi: 10.1117/12.2006795
Show Author Affiliations
Carole Amiot, Thales Electron Devices (France)
GIPSA-Lab (France)
Jérémie Pescatore, Thales Electron Devices (France)
Jocelyn Chanussot, GIPSA-Lab (France)
Michel Desvignes, GIPSA-Lab (France)

Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

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