Biomedical subjects

Vincent Barra

Publications and source records attributed to Vincent Barra.

4 recordsLinked to original sources

PubMed2006

Robust segmentation and analysis of DNA microarray spots using an adaptative split and merge algorithm.

Microarray images push to their limits classical analysis methods, since gene spots are often poorly contrasted, ill defined and of irregular shapes. These characteristics hinder a robust quantification of corresponding values for red and green intensities as well as their R/G ratio. New approaches are thus needed to ensure accurate data extraction from these images. Herein we present an automatic non-supervised algorithm for a fast and accurate spot data extraction from DNA microarrays. The method is based on a split and merge algorithm, relying on a Delaunay triangulation process, allowing an incremental partition of the image into homogeneous polygons. Geometric properties of triangles as well as homogeneity criteria are defined according to the specificities of microarray image signals. The method is first assessed on simulated data, and then compared with GenePix and Jaguar Softwares. Results in segmentation and quantification are superior to those obtained from a number of standard techniques for spot extraction.

Algorithms↗

PubMed2004

Analysis of gene expression data using functional principal components.

The large amount of data involved in DNA microarrays implies the development of efficient computer algorithms to analyze the gene expressions, and thus to study the transcriptome. Numerous techniques already exist and we propose a new method based on the key idea that gene profiles may be considered as continuous curves. The analysis of the set of curves stemming from the DNA microarray may be then performed using a functional analysis which can exhibit the main modes of variations in this set, gather genes with similar variations and extract characteristic parameters of gene profiles. We aim here at introducing this method, called the Functional Principal Component Analysis. A prospective study has been performed on two available datasets, concerning on the one hand the sporulation data of the Saccharomyces cerevisiae, and on the other hand data of tumor cell lines. Results are very promising: the method is able to extract characteristic parameters from the datasets, to extract significant modes of variations in the set of gene profiles, and to link these variations to biological processes already studied in literature.

Algorithms↗

PubMed2002

Automatic volumetric measurement of lateral ventricles on magnetic resonance images with correction of partial volume effects.

PURPOSE: To propose a method for the quantification of lateral ventricle (LV) volumes on a single sequence of 3D magnetic resonance (MR) images. MATERIALS AND METHODS: This algorithm, following a preliminary fuzzy tissue classification step, is based on the development of mathematical morphology processes allowing both the extraction of the LVs and the correction of partial volume effects on their boundaries. The procedure is fast and totally unsupervised. The method is tested on a phantom image, then applied to five patients diagnosed as potentially suffering from Alzheimer's disease, and finally applied on several MR acquisitions to show the genericness of the algorithm. RESULTS AND CONCLUSION: This technique yielded both an accurate estimation of ventricular volumes intra- and intersubject with respect to published data and a relevant management of partial volume effects. Numerous clinical applications are now expected, from the study of schizophrenia to the longitudinal follow-up of Alzheimer's patients.

Aged↗

PubMed2002

Segmentation of fat and muscle from MR images of the thigh by a possibilistic clustering algorithm.

Physical training is proved to induce changes in physical capacity and body composition. We propose in this article a fast, unsupervised and fully three-dimensional automatic method to extract muscle and fat volumes from magnetic resonance images of thighs in order to assess these changes. The technique relies on the use of a fuzzy clustering algorithm and post-processings to accurately process the body composition of thighs. Results are compared on 11 healthy voluntary elderly people with those provided on the same data by a validated method already published, and its reliability is assessed on repeated measures on three subjects. The two methods statistically agree when computing muscle and fat volumes, and clinical implications of this fully automatic method are important for medicine, physical conditioning, weight-loss programs and predictions of optimal body weight.

Algorithms↗