Biomedical subjects

Guillaume Delyon

Publications and source records attributed to Guillaume Delyon.

3 recordsLinked to original sources

PubMed2006

Minimal stochastic complexity image partitioning with unknown noise model.

We present a generalization of a new statistical technique of image partitioning into homogeneous regions to cases where the family of the probability laws of the gray-level fluctuations is a priori unknown. For that purpose, the probability laws are described with step functions whose parameters are estimated. This approach is based on a polygonal grid which can have an arbitrary topology and whose number of regions and regularity of its boundaries are obtained by minimizing the stochastic complexity of the image. We demonstrate that efficient homogeneous image partitioning can be obtained when no parametric model of the probability laws of the gray levels is used and that this approach leads to a criterion without parameter to be tuned by the user. The efficiency of this technique is compared to a statistical parametric technique on a synthetic image and is compared to a standard unsupervised segmentation method on real optical images.

Algorithms↗

PubMed2004

Photon-noise effect on detection in coherent active images.

We analyze photon-noise effects on target detection performance in low-flux coherent active imagery systems. We show that when photon noise is expected, the performance of classical detection techniques designed for pure and fully developed speckle images can be improved with no increase in algorithm complexity. Furthermore, the mean photon number under which photon noise becomes sensitive is higher when the target and background mean values are unknown than in the idealized case, where they are assumed to be known, and when the reflectivity ratio between the target and the background is low.

Journal Article↗

PubMed2004

Bhattacharyya distance as a contrast parameter for statistical processing of noisy optical images.

In many imaging applications, the measured optical images are perturbed by strong fluctuations or boise. This can be the case, for example, for coherent-active or low-flux imagery. In such cases, the noise is not Gaussian additive and the definition of a contrast parameter between two regions in the image is not always a straightforward task. We show that for noncorrelated noise, the Bhattacharyya distance can be an efficient candidate for contrast definition when one uses statistical algorithms for detection, location, or segmentation. We demonstrate with numerical simulations that different images with the same Bhattacharyya distance lead to equivalent values of the performance criterion for a large number of probability laws. The Bhattacharyya distance can thus be used to compare different noisy situations and to simplify the analysis and the specification of optical imaging systems.

Journal Article↗