Comp-denoiser adapted to coronary X-ray images

International audienceCompression of coronary angiographic images has been shown to be difficult as compared to other medical imaging modalities. Some of the factors partially responsible for this is the presence of complex detail structures that are only apparent by subtle changes in the contrast and altered by significant amount of noise. Simultaneous compression and denoising is required when images are altered by additive noise. In our work we developed a wavelet based comp-denoiser adapted to coronary X-ray images. The proposed approach consists on integrating an inter-scale dependant thresholding function, using Bayesian estimation theory, with WTCQ coding algorithm. Experimental results show that despite its simplicity and computational efficiency, our method yields a higher compression performance with a superior image quality. It also outperforms the state of the art of compression based denoisers in terms of distortion

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

SIS 2017. Statistics and Data Science: new challenges, new generations

The 2017 SIS Conference aims to highlight the crucial role of the Statistics in Data Science. In this new domain of ‘meaning’ extracted from the data, the increasing amount of produced and available data in databases, nowadays, has brought new challenges. That involves different fields of statistics, machine learning, information and computer science, optimization, pattern recognition. These afford together a considerable contribute in the analysis of ‘Big data’, open data, relational and complex data, structured and no-structured. The interest is to collect the contributes which provide from the different domains of Statistics, in the high dimensional data quality validation, sampling extraction, dimensional reduction, pattern selection, data modelling, testing hypotheses and confirming conclusions drawn from the data

Wavelet based Compression Denoising of coronary X-Ray Images

International audienceCompression of angiographic images has been shown to be difficult when compared with other medical imaging modalities. The factors partially responsible for this are the presence of complex structures that are only apparent by subtle changes in the contrast and the significant amount of acquisition noise. In this work, we propose a Comp–Denoiser adapted to coronary X-ray images. For this purpose, Wavelet-based Trellis Coded Quantisation (WTCQ) algorithm is extended to incorporate a bivariate thresholding that considers dependencies between wavelet coefficients and their parents in coarser sub-bands. Experimental results show that despite its simplicity our method yields high compression performance