Biomedical subjects

François Destrempes

Publications and source records attributed to François Destrempes.

5 recordsLinked to original sources

PubMed2006

Fusion of Hidden Markov Random Field models and its Bayesian estimation.

In this paper, we present a Hidden Markov Random Field (HMRF) data-fusion model. The proposed model is applied to the segmentation of natural images based on the fusion of colors and textons into Julesz ensembles. The corresponding Exploration/ Selection/Estimation (ESE) procedure for the estimation of the parameters is presented. This method achieves the estimation of the parameters of the Gaussian kernels, the mixture proportions, the region labels, the number of regions, and the Markov hyper-parameter. Meanwhile, we present a new proof of the asymptotic convergence of the ESE procedure, based on original finite time bounds for the rate of convergence.

Algorithms↗

PubMed2005

Three-dimensional biplanar reconstruction of scoliotic rib cage using the estimation of a mixture of probabilistic prior models.

In this paper, we present an original method for the three-dimensional (3-D) reconstruction of the scoliotic rib cage from a planar and a conventional pair of calibrated radiographic images (postero-anterior with normal incidence and lateral). To this end, we first present a robust method for estimating the model parameters in a mixture of probabilistic principal component analyzers (PPCA). This method is based on the stochastic expectation maximization (SEM) algorithm. Parameters of this mixture model are used to constrain the 3-D biplanar reconstruction problem of scoliotic rib cage. More precisely, the proposed PPCA mixture model is exploited for dimensionality reduction and to obtain a set of probabilistic prior models associated with each detected class of pathological deformations observed on a representative training scoliotic rib cage population. By using an appropriate likelihood, for each considered class-conditional prior model, the proposed 3-D reconstruction is stated as an energy function minimization problem, which is solved with an exploration/selection algorithm. The optimal 3-D reconstruction then corresponds to the class of deformation and parameters leading to the minimal energy. This 3-D method of reconstruction has been successfully tested and validated on a database of 20 pairs of biplanar radiographic images of scoliotic patients, yielding very promising results. As an alternative to computed tomography-scan 3-D reconstruction this scheme has the advantage of low radiation for the patient, and may also be used for diagnosis and evaluation of deformity of a scoliotic rib cage. The proposed method remains sufficiently general to be applied to other reconstruction problems for which a database of objects to be reconstructed is available (with two or more radiographic views).

Algorithms↗

PubMed2005

A stochastic method for Bayesian estimation of hidden Markov random field models with application to a color model.

We propose a new stochastic algorithm for computing useful Bayesian estimators of hidden Markov random field (HMRF) models that we call exploration/selection/estimation (ESE) procedure. The algorithm is based on an optimization algorithm of O. François, called the exploration/selection (E/S) algorithm. The novelty consists of using the a posteriori distribution of the HMRF, as exploration distribution in the E/S algorithm. The ESE procedure computes the estimation of the likelihood parameters and the optimal number of region classes, according to global constraints, as well as the segmentation of the image. In our formulation, the total number of region classes is fixed, but classes are allowed or disallowed dynamically. This framework replaces the mechanism of the split-and-merge of regions that can be used in the context of image segmentation. The procedure is applied to the estimation of a HMRF color model for images, whose likelihood is based on multivariate distributions, with each component following a Beta distribution. Meanwhile, a method for computing the maximum likelihood estimators of Beta distributions is presented. Experimental results performed on 100 natural images are reported. We also include a proof of convergence of the E/S algorithm in the case of nonsymmetric exploration graphs.

Algorithms↗

PubMed2004

A statistical model for contours in images.

In this paper, we describe a statistical model for the gradient vector field of the gray level in images validated by different experiments. Moreover, we present a global constrained Markov model for contours in images that uses this statistical model for the likelihood. Our model is amenable to an Iterative Conditional Estimation (ICE) procedure for the estimation of the parameters; our model also allows segmentation by means of the Simulated Annealing (SA) algorithm, the Iterated Conditional Modes (ICM) algorithm, or the Modes of Posterior Marginals (MPM) Monte Carlo (MC) algorithm. This yields an original unsupervised statistical method for edge-detection, with three variants. The estimation and the segmentation procedures have been tested on a total of 160 images. Those tests indicate that the model and its estimation are valid for applications that require an energy term based on the log-likelihood ratio. Besides edge-detection, our model can be used for semiautomatic extraction of contours, localization of shapes, non-photo-realistic rendering; more generally, it might be useful in various problems that require a statistical likelihood for contours.

Algorithms↗

PubMed2002

Topography-based screening for previous laser in situ keratomileusis to correct myopia.

PURPOSE: To demonstrate the feasibility of developing a screening tool based on corneal topography to detect previous myopic laser in situ keratomileusis (LASIK). SETTING: Clinical data from a private clinic analyzed in a university setting. METHODS: Two hundred thirty-three topographies were randomly selected so 1 topography per patient was used: 150 from unoperated corneas and 83 from corneas that had LASIK to correct myopia. The mean surgical correction was -4.40 diopters (D) +/- 2.53 (SD) (range -11.00 to -0.38 D). All topographies were performed using an Orbscan II unit (Bausch & Lomb Surgical). The LASIK procedures were performed using a Technolas 217C excimer laser and a Hansatome microkeratome (Bausch & Lomb Surgical). The algorithms used the mean value of the directional derivative (DT) of the anterior tangential curvature of the cornea in the 2.2 mm radius central disk and the mean value of the anterior elevation (E) with respect to the best-fit sphere in the 0.5 mm radius central disk. Topographies in the testing set (n = 119) were classified as operated if E < 0 (E algorithm) or DT > 0 (DT algorithm) or as unoperated. RESULTS: The E algorithm yielded 0% false positives and 16.7% false negatives and the DT algorithm, 6.5% and 7.1%, respectively. For myopia greater than -1.12 D, the DT algorithm provided a 0% false negative rate. The performance of E and DT algorithms, used in combination, was superior to clinical assessment. CONCLUSION: Criteria based on Orbscan II corneal topography are proposed for the detection of previous myopic LASIK performed with a Technolas 217C excimer laser.

Adolescent↗