Fully Automatic Segmentation of Brain Tumor Images using Support Vector Machine Classiffication in Combination with Hierarchical Conditional Random Field Regularization

Delineating brain tumor boundaries from magnetic resonance images is an essential task for the analysis of brain cancer. We propose a fully automatic method for brain tissue segmentation, which combines Support Vector Machine classification using multispectral intensities and textures with subsequent hierarchical regularization based on Conditional Random Fields. The CRF regularization introduces spatial constraints to the powerful SVM classification, which assumes voxels to be independent from their neighbors. The approach first separates healthy and tumor tissue before both regions are subclassified into cerebrospinal fluid, white matter, gray matter and necrotic, active, edema region respectively in a novel hierarchical way. The hierarchical approach adds robustness and speed by allowing to apply different levels of regularization at different stages. The method is fast and tailored to standard clinical acquisition protocols. It was assessed on 10 multispectral patient datasets with results outperforming previous methods in terms of segmentation detail and computation times.

Hierarchical Markov Random Fields Applied to Model Soft Tissue Deformations on Graphics Hardware

Many methodologies dealing with prediction or simulation of soft tissue deformations on medical image data require preprocessing of the data in order to produce a different shape representation that complies with standard methodologies, such as mass–spring networks, finite element method s (FEM). On the other hand, methodologies working directly on the image space normally do not take into account mechanical behavior of tissues and tend to lack physics foundations driving soft tissue deformations. This chapter presents a method to simulate soft tissue deformations based on coupled concepts from image analysis and mechanics theory. The proposed methodology is based on a robust stochastic approach that takes into account material properties retrieved directly from the image, concepts from continuum mechanics and FEM. The optimization framework is solved within a hierarchical Markov random field (HMRF) which is implemented on the graphics processor unit (GPU See Graphics processing unit ).

Distributed Control Nodes for Material Flow System Controls on the Example of Unit Load Conveyor and Sorter Facilities

The Chair of Transportation and Ware-housing at the University of Dortmund together with its industrial partner has developed and implemented a decentralized control system based on embedded technology and Internet standards. This innovative, highly flexible system uses autonomous software modules to control the flow of unit loads in real-time. The system is integrated into Chair’s test facility consisting of a wide range of conveying and sorting equipment. It is built for proof of concept purposes and will be used for further research in the fields of decentralized automation and embedded controls. This presentation describes the implementation of this decentralized control system.

Hebbian analysis of the transformation of medial entorhinal grid-cell inputs to hippocampal place fields.

The discovery of grid cells in the medial entorhinal cortex (MEC) permits the characterization of hippocampal computation in much greater detail than previously possible. The present study addresses how an integrate-and-fire unit driven by grid-cell spike trains may transform the multipeaked, spatial firing pattern of grid cells into the single-peaked activity that is typical of hippocampal place cells. Previous studies have shown that in the absence of network interactions, this transformation can succeed only if the place cell receives inputs from grids with overlapping vertices at the location of the place cell's firing field. In our simulations, the selection of these inputs was accomplished by fast Hebbian plasticity alone. The resulting nonlinear process was acutely sensitive to small input variations. Simulations differing only in the exact spike timing of grid cells produced different field locations for the same place cells. Place fields became concentrated in areas that correlated with the initial trajectory of the animal; the introduction of feedback inhibitory cells reduced this bias. These results suggest distinct roles for plasticity of the perforant path synapses and for competition via feedback inhibition in the formation of place fields in a novel environment. Furthermore, they imply that variability in MEC spiking patterns or in the rat's trajectory is sufficient for generating a distinct population code in a novel environment and suggest that recalling this code in a familiar environment involves additional inputs and/or a different mode of operation of the network.

Composition of organic matter from samples of massive sulfides, bottom sediments, and seawater collected within the Logachev and Broken Spur hydrothermal fields, Mid-Atlantic Ridge

Lipid components of hydrothermal deposits from the unusual field at 14°45'N MAR and from the typical field at 29°N MAR were studied. For the first time mixed nature of organic matter (OM) from hydrothermal sulfide deposits was established with use of biochemical, gas chromatographic, and molecular methods of studies. In composition of OM lipids of phytoplankton, those of chemosynthesis bacteria and non-biogenic synthesis lipids were determined. Specific conditions of localization of sulfide deposits originated from ''black smokers'' (reducing conditions, absence of free oxygen, presence of reduced sulfur preventing OM from decomposition) let biogenic material, including bacterial one, be preserved in sulfide deposits. The hydrothermal system at 14°45'N MAR is characterized by geological, geochemical and thermodynamic conditions allowing abiogenic synthesis of methane and petroleum hydrocarbons. For sulfide deposits at 29°N and other active hydrothermal fields known at MAR, abiogenic synthesis of hydrocarbons occurs in lower scales.