AF641, AI954 and AW954 antibodies label the endoplasmic reticulum by immunofluorescence

The AF641, AI954 and AW954 recombinant antibodies detect different markers of the endoplasmic reticulum by immunofluorescence in HEK293 cells

AF641, AI954 and AW954 antibodies label markers of the human endoplasmic reticulum by western blot

The recombinant antibodies AF641, AI954 and AW954 detect different markers of the endoplasmic reticulum by western blot

AD822, AW199, AW200 and AW201 antibodies against the human Fas receptor (CD95) bind the surface of HEK293 cells as revealed by flow cytometry

The recombinant antibodies AD822, AW199, AW200 and AW201 bind HEK293 cells expressing the human Fas receptor protein as assessed by flow cytometry

RB599, RB600, RB601, RB602, RB603 and RB605 antibodies recognize a mouse Claudin-9 peptide by ELISA

The recombinant antibodies RB599, RB600, RB601, RB602, RB603 and RB605 detect by ELISA a synthetic peptide from the mouse Claudin-9 protein

De odyssee van den. Knjas poteiafcin. Naverteld uit net dagboeic van Kir ill, lid van het revolutionair Scheepsfcomitee.

OPLADEN-RUG0

Image Statistics based on Diffeomorphic Matching

We propose a new approach to deal with the first and second order statistics of a set of images. These statistics take into account the images characteristic deformations and their variations in intensity. The central algorithm is based on non-supervised diffeomorphic image matching (without landmarks or human intervention). As they convey the notion of the mean shape and colors of an object and the one of its common variations, such statistics of sets of images may be relevant in the context of object recognition, both in the segmentation of any of its representations and in the classification of them. The proposed approach has been tested on a small database of face images to compute a mean face and second order statistics. The results are very encouraging since, wheras the algorithm does not need any human intervention and is not specific to face image databases, the mean image looks like a real face and the characteristic modes of variation (deformation and intensity changes) are sensible

Imaging Methods for MEG/EEG Inverse Problem

Recovering electrical activity of the brain from MEG/EEG measurements is known as the MEEG inverse problem. It is an ill-posed problem in several senses. One is that there is further less data observed than data to recover. One way to address this issue is to search for regular solutions. We present here a framework for applying image processing filtering techniques to the MEEG inverse problem. Exprimentations are presented on synthetic dara and validation is carried out on one real MEG data set

A Benchmark Framework for Multiregion Analysis of Vesselness Filters

International audienceThis paper is an updated version of [1], following the correction of numerical errors. Vessel enhancement (aka vesselness) filters, are part of angiographic image processing for more than twenty years. Their popularity comes from their ability to enhance tubular structures while filtering out other structures, especially as a preliminary step of vessel segmentation. Choosing the right vesselness filter among the many available can be difficult, and their parametrization requires an accurate understanding of their underlying concepts and a genuine expertise. In particular, using default parameters is often not enough to reach satisfactory results on specific data. Currently, only few benchmarks are available to help the users choosing the best filter and its parameters for a given application. In this article, we present a generic framework to compare vesselness filters. We use this framework to compare seven gold standard filters. Our experiments are performed on three public datasets: the hepatic Ircad dataset (CT images), the Bullit dataset (brain MRA images) and the synthetic VascuSynth dataset. We analyse the results of these seven filters both quantitatively and qualitatively. In particular, we assess their performances in key areas: the organ of interest, the whole vascular network neighbourhood and the vessel neighbourhood split into several classes, based on their diameters. We also focus on the vessels bifurcations, which are often missed by vesselness filters. We provide the code of the benchmark, which includes upto-date C++ implementations of the seven filters, as well as the experimental setup (parameter optimization, result analysis, etc.). An online demonstrator is also provided to help the community apply and visually compare these vesselness filters