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Improving sensor noise analysis for CT-Scanner identification

Anas Kharboutly 1 William Puech 1 Gérard Subsol 1 Denis Hoa 2
1 ICAR - Image & Interaction
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
Abstract : CT-Scanner devices produce three-dimensional images of the internal structure of the body. In this paper, we propose a method that is based on the analysis of sensor noise to identify the CT-Scanner device. For each CT-scanner we built a reference pattern noise and a correlation map from its slices. Finally, we can correlate any test slice with the reference pattern noise of each device according to its correlation map. This correlation map gives a weighting for each pixel regarding its position in the reference pattern noise. We used a wavelet-based Wiener filter and an edge detection method to extract the noise from a slice. Experiments were applied on three CT-Scanners with 40 3D images, including 3600 slices, and we demonstrate that we are able to identify each CT-Scanner separately.
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Anas Kharboutly, William Puech, Gérard Subsol, Denis Hoa. Improving sensor noise analysis for CT-Scanner identification. EUSIPCO: European Signal Processing Conference, Aug 2015, Nice, France. pp.2411-2415, ⟨10.1109/EUSIPCO.2015.7362817⟩. ⟨lirmm-01379558⟩



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