Intensity Segmentation of the Human Brain with Tissue dependent Homogenization

High-precision segmentation of the human cerebral cortex based on T1-weighted MRI is still a challenging task. When opting to use an intensity based approach, careful data processing is mandatory to overcome inaccuracies. They are caused by noise, partial volume effects and systematic signal intensity variations imposed by limited homogeneity of the acquisition hardware. We propose an intensity segmentation which is free from any shape prior. It uses for the first time alternatively grey (GM) or white matter (WM) based homogenization. This new tissue dependency was introduced as the analysis of 60 high resolution MRI datasets revealed appreciable differences in the axial bias field corrections, depending if they are based on GM or WM. Homogenization starts with axial bias correction, a spatially irregular distortion correction follows and finally a noise reduction is applied. The construction of the axial bias correction is based on partitions of a depth histogram. The irregular bias is modelled by Moody Darken radial basis functions. Noise is eliminated by nonlinear edge preserving and homogenizing filters. A critical point is the estimation of the training set for the irregular bias correction in the GM approach. Because of intensity edges between CSF (cerebro spinal fluid surrounding the brain and within the ventricles), GM and WM this estimate shows an acceptable stability. By this supervised approach a high flexibility and precision for the segmentation of normal and pathologic brains is gained. The precision of this approach is shown using the Montreal brain phantom. Real data applications exemplify the advantage of the GM based approach, compared to the usual WM homogenization, allowing improved cortex segmentation

Aid policy of the industrial countries

Time and again, and in the more recent past during the UNCTAD Ill-Conference, the less developed countries (LDCs) have found fault with the industrial countries’ declining contribution towards the economic development of the Third World. The question is now whether the future will bring a change of this tendency which has shown itself for a number of years

Segmentierung des Gehirns auf der Basis von MR-Daten

Es wird ein Segmentierungsverfahren vorgestellt, das bei T1-gewichteten MR Aufnahmen Liquor, Cortex und weisse Materie trennt. Das Verfahren korrigiert in mehreren Schritten aufnahmetechnisch bedingte Artefakte und bestimmt die Substanzen durch 2 globale Schwellen. Das Verfahren erfordert an mehreren Stellen eine interaktive Justierung von Parametern und ist entsprechend flexibel

Noise Reduction in Images: Some Recent Edge-Preserving Methods

We introduce some recent and very recent smoothing methods which focus on the preservation of boundaries, spikes and canyons in presence of noise. We try to point out basic principles they have in common; the most important one is the robustness aspect. It is reflected by the use of `cup functions' in the statistical loss functions instead of squares; such cup functions were introduced early in robust statistics to down weight outliers. Basically, they are variants of truncated squares. We discuss all the methods in the common framework of `energy functions', i.e we associate to (most of) the algorithms a `loss function' in such a fashion that the output of the algorithm or the `estimate' is a global or local minimum of this loss function. The third aspect we pursue is the correspondence between loss functions and their local minima and nonlinear filters. We shall argue that the nonlinear filters can be interpreted as variants of gradient descent on the loss functions. This way we can show that some (robust) M-estimators and some nonlinear filters produce almost the same result

Polyglycerol coated polypropylene surfaces for protein and bacteria resistance

Polyglycerol (PG) coated polypropylene (PP) films were synthesized in a two- step approach that involved plasma bromination and subsequently grafting hyperbranched polyglycerols with very few amino functionalities. The influence of different molecular weights and density of reactive linkers were investigated for the grafted PGs. Longer bromination times and higher amounts of linkers on the surface afforded long-term stability. The protein adsorption and bacteria attachment of the PP-PG films were studied. Their extremely low amine content proved to be beneficial for preventing bacteria attachment

Multiscale Convolutional Neural Networks for Vision–Based Classification of Cells

International audienceWe present a Multiscale Convolutional Neural Network (MCNN) approach for vision-based classification of cells. Based on several deep Convolutional Neural Networks (CNN) acting at different resolutions, the proposed architecture avoid the classical handcrafted features extraction step, by processing features extraction and classification as a whole. The proposed approach gives better classification rates than classical state-of-the-art methods allowing a safer Computer-Aided Diagnosis of pleural cancer