No abstract

No abstract

Optical microscopy allows us to study living fluorescent biological samples. Optical sectioning is a technique to obtain three-dimensional (3D) information about the observed object by acquiring a stack of two-dimensional (2D) images at different depths through the sample. However, the specific shape of the 3D optical transfer function of the optical microscope leads to images presenting defects, such as, for example, an apparent elongation along the vertical axis. It is therefore necessary to preprocess the images before any quantitative measurement is performed. This image restoration can be obtained by deconvolution of the acquired 3D image. We have tested several deconvolution algorithms on synthetic images, obtained by convolution of a solid sphere with a measured point spread function. We have compared the restored image with the original one (shape and volume). The linear least-squares method is fast, but artefacts are still present in the restored images. The Carrington method is well adapted to thin objects. The maximum likelihood-expectation maximization method leads to a good reconstruction of the object, but is very time consuming.Résumé. La microscopie optique permet l'étude de spécimens biologiques vivants et fluorescents. La technique par coupes sériées donne des informations tridimensionnelles (3D) sur l'objetétudié par l'acquisition d'une pile d'images bidimensionnellesà différentes profondeurs de focalisationà travers l'échantillon. Les spécificités de la fonction de transfert optique 3D du microscope conduisentà des images présentant des défauts, comme par exemple uneélongation apparente selon l'axe vertical. Il est donc nécessaire de traiter les images avant toute mesure quantitative. On procèdeà une déconvolution de l'image 3D obtenue. Nous avons testé différents algorithmes de déconvolution sur des images de synthèse obtenues par convolution d'une bille pleine avec une fonction de transfert optique mesurée. Nous avons comparé, en forme et en volume, les images restaurées avec l'image d'origine. La méthode 'linear least square' est rapide, mais l'image restaurée présente des artefacts. La méthode de Carrington est bien adaptéeà la restauration d'objets fins. La méthode 'maximum likelihood-expectation maximization' permet une bonne reconstruction des images, mais demande de grands temps de calcul.

We present a case study that uses high performance workstations and a Virtual Reality display system to process large amounts of geometry data for real-time 3D exploration, superimposition, and interactive navigational tasks in a virtual reality environment.

We present a systematic technique for extraction of useful information from large-scale neural data in the context of brain-computer interfaces. The technique is based on a direct linear discriminant analysis, recently developed for face recognition problems. We show that this technique is capable of extracting useful information from brain data in a systematic fashion and can serve as a general analytical tool for other types of biomedical data, such as images and collections of images (movies). The performance of the method is tested on intracranial electroencephalographic data recorded from the human brain.