Fusion of heterogeneous and noisy informations: application to the quantification of the coronary stenosis

Proposes an algorithm to evaluate the similarity between two space-time distributions. One is obtained by experiment; the other is estimated by a numerical calculus. These are heterogeneous information types; their locations, as well as their densities and their reliabilities, are various. We have developed a first method which is correct when numerical and experimental information are closely linked. Nevertheless, in real-world problems, the initial conditions which induce the numerical information are vague. For the nonlinearity of the studied phenomena, the degree of similarity of both types of information is deeply degraded. Our approach is robust relative to this noise. It is used as part of a coronary stenosis identification process.