Mr. Arnaud Boucher Profile

Arnaud Boucher

at Univ Paris Descartes

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Publications (3)

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Proceedings Article | 16 July 2019 Paper

Light spatial distribution calibration based on local density estimation for reflectance transformation imaging

Yuly Castro, Gilles Pitard, Abir Zendagui, Gaëtan Le Göic, Vincent Brost, Arnaud Boucher, Alamin Mansouri

Proceedings Volume 11172, 111720A (2019) https://doi.org/10.1117/12.2521849

KEYWORDS: Light sources, Visualization, Inspection, Calibration, Data modeling, Bidirectional reflectance transmission function, Image processing

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Reflectance Transformation Imaging (RTI) is a multi-light-based imaging technique that can provide relevant information on both local micro-geometry and visual appearance of a studied surface. The local angular reflectance is modelled to allow the relighting of the surface appearance under any arbitrary light direction. The methods used to model the local reflectance of each pixel are mainly PTM (2nd order polynomial functions), HSH (Hemispherical Harmonics) and more recently DMD (Dissrete Modal Decomposition). For all these methods, a uniform distribution of the light positions over the hemisphere is an implicit hypothesis. However, it’s impossible to satisfy this condition in practice. As a result of this non-homogeneous distribution, several artifacts can affect the reconstruction and alter the quality of the visual appearance assessment. To address this issue, we proposed a methodology consisting in the estimation of the spatial distribution of the lighting directions used during RTI acquisitions, based on a local density estimation. These local density values are then used to weight the Least Squares regression, and thus to correct the contributions of each image of the RTI acquisitions. This methodology is applied on two metallic surfaces with visual singularities. From presented results, it can be concluded that it is necessary to take into account this non-uniformity in order not to alter the quality of RTI data and subsequent inspection tasks.

Proceedings Article | 6 March 2013 Paper

Mammogram CAD, hybrid registration and iconic analysis

A. Boucher, F. Cloppet, N. Vincent

Proceedings Volume 8661, 86610J (2013) https://doi.org/10.1117/12.2008512

KEYWORDS: Image registration, Breast, Mammography, Tissues, Image segmentation, Computer aided diagnosis and therapy, Image analysis, Associative arrays, Chemical species, Image processing

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This paper aims to develop a computer aided diagnosis (CAD) based on a two-step methodology to register and analyze pairs of temporal mammograms. The concept of "medical file", including all the previous medical information on a patient, enables joint analysis of different acquisitions taken at different times, and the detection of significant modifications. The developed registration method aims to superimpose at best the different anatomical structures of the breast. The registration is designed in order to get rid of deformation undergone by the acquisition process while preserving those due to breast changes indicative of malignancy. In order to reach this goal, a referent image is computed from control points based on anatomical features that are extracted automatically. Then the second image of the couple is realigned on the referent image, using a coarse-to-fine approach according to expert knowledge that allows both rigid and non-rigid transforms. The joint analysis detects the evolution between two images representing the same scene. In order to achieve this, it is important to know the registration error limits in order to adapt the observation scale. The approach used in this paper is based on an image sparse representation. Decomposed in regular patterns, the images are analyzed under a new angle. The evolution detection problem has many practical applications, especially in medical images. The CAD is evaluated using recall and precision of differences in mammograms.

Proceedings Article | 26 February 2013 Paper

A pairwise image analysis with sparse decomposition

A. Boucher, F. Cloppet, N. Vincent

Proceedings Volume 8670, 867020 (2013) https://doi.org/10.1117/12.2007876

KEYWORDS: Tissues, Associative arrays, Mammography, Chemical species, Image processing, Medical imaging, Signal processing, Image registration, Image analysis, Computer aided diagnosis and therapy

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This paper aims to detect the evolution between two images representing the same scene. The evolution detection problem has many practical applications, especially in medical images. Indeed, the concept of a patient “file” implies the joint analysis of different acquisitions taken at different times, and the detection of significant modifications. The research presented in this paper is carried out within the application context of the development of computer assisted diagnosis (CAD) applied to mammograms. It is performed on already registered pair of images. As the registration is never perfect, we must develop a comparison method sufficiently adapted to detect real small differences between comparable tissues. In many applications, the assessment of similarity used during the registration step is also used for the interpretation step that yields to prompt suspicious regions. In our case registration is assumed to match the spatial coordinates of similar anatomical elements. In this paper, in order to process the medical images at tissue level, the image representation is based on elementary patterns, therefore seeking patterns, not pixels. Besides, as the studied images have low entropy, the decomposed signal is expressed in a parsimonious way. Parsimonious representations are known to help extract the significant structures of a signal, and generate a compact version of the data. This change of representation should allow us to compare the studied images in a short time, thanks to the low weight of the images thus represented, while maintaining a good representativeness. The good precision of our results show the approach efficiency.

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