Maurice Delplanque

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The exposure index (EI) gives a feedback to radiographers on the image quality in digital radiography, but its estimation on clinical images raises many challenges. In this paper we provide a critical overview of state of the art methods that address this problem and we show that more robust results can be obtained by detecting anatomical structures. This(More)
Denoising and contrast enhancement play key roles in optimizing the trade-off between image quality and X-ray dose. However, these tasks present multiple challenges raised by noise level, low visibility of fine anatomical structures, heterogeneous conditions due to different exposure parameters, and patient characteristics. This work proposes a new method(More)
A denoising method is proposed for full body X-ray images, acquired under low dose conditions. The suggested algorithm is based on a non local means filter adapted to the statistics of Poisson noise. A new feature of the method is to locally set the filtering parameters in order to denoise while preserving details in low absorption regions. Thus, we propose(More)
Marginal, often contaminated, sites exist in large areas across the world as a result of historic activities such as industry, transportation and mineral extraction. Remediation, or other improvements, of these sites is typically only considered for sites with high exploitation pressure and those posing the highest risks to human health or the environment.(More)
Purpose: The exposure index is an important measure used in digital radiography to control the dose at the detector. This value should be computed in regions of interest that are adapted to each patient’s anatomy and pose. Material and methods: We propose to define automatically these regions based on anatomical landmarks in the main structures of interest(More)
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