Ecumenically, the fastest growing segment of Big Data is human biology-related data and
the annual data creation is on the order of zetabytes. The implications are global across
industries, of which the treatment of brain related illnesses and trauma could see the
most significant and immediate effects. The next generation of health care IT and sensory
devices are acquiring and storing massive amounts of patient related data. An innovative
Brain-Computer Interface (BCI) for interactive 3D visualization is presented utilizing the
Hadoop Ecosystem for data analysis and storage. The BCI is an implementation of Bayesian
factor analysis algorithms that can distinguish distinct thought actions using magneto
encephalographic (MEG) brain signals. We have collected data on five subjects yielding 90%
positive performance in MEG mid- and post-movement activity. We describe a driver that
substitutes the actions of the BCI as mouse button presses for real-time use in visual
simulations. This process has been added into a flight visualization demonstration. By
thinking left or right, the user experiences the aircraft turning in the chosen direction.
The driver components of the BCI can be compiled into any software and substitute a
user’s intent for specific keyboard strikes or mouse button presses. The
BCI’s data analytics of a subject’s MEG brainwaves and flight visualization
performance are stored and analyzed using the Hadoop Ecosystem as a quick retrieval data
warehouse.
We have developed a visualization system, named Atomsviewer, to render a billion atoms from the results of a molecular dynamics simulation. This system uses a hierarchical view frustum culling algorithm based on the octree data structure to efciently remove atoms that are outside of the eld of view. A novel occlusion culling algorithm, using a probability function, then selects atoms with a high probability of being visible. These selected atoms are further tested with a traditional occlusion culling algorithm before being rendered as spheres at varying levels of detail. To achieve scalability, Atomsviewer is distributed over a cluster of PCs that execute a parallelized version of the hierarchical view frustum culling and the probabilistic occlusion culling, and a graphics workstation that renders the atoms. We have used Atomsviewer to render a billion-atom data set on a dual processor SGI Onyx2 with an In niteReality2 graphics pipeline connected to a four-node PC cluster.
A fully automated application was developed and used for the registration of T1-weighted magnetic resonance images (MRIs) for Alzheimer patients. Two methods for image registration were implemented and compared: affine and nonlinear registration. Nonlinear registration uses continuum-mechanics-based elastic deformation. The affine registration algorithm is linear and is generated by an amplitude-modulated phase-only filter. The nonlinear registration method uses an elastic transformation generated by Navier-Stokes continuum-mechanics models. The validation method to quantitatively compare the performance of the affine and nonlinear registration algorithms uses root-mean-square error and three-dimensional volume rendering.
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