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Tractography on HARDI data
(2011)
Diffusion weighted imaging is an important modality in clinical imaging and the only possibility to gain insight into the human brain noninvasively and in-vivo. The applications of this imaging technique are diversified. It is used to study the brain, its structure, development and the functionality of the different areas. Further, important fields of application are neurosurgical planning, examinations of pathologies, investigation of Alzheimer-, strokes, and multiple sclerosis. This thesis gives a brief introduction to MRI and diffusion MRI. Based on this, the mostly used data representation in diffusion MRI in clinical imaging, the diffusion tensor, is introduced. As the diffusion tensor suffers from severe limitations new techniques subsumed under the term HARDI (high angular resolution diffusion imaging) are introduced and discussed in detail. Further, an extensive introduction to tractography, approaches that aim at reconstructing neuronal fibers, is given. Based on the knowledge fromthe theoretical part established tractography algorithms are redesigned to handle HARDI data and, thus, improve the reconstruction of neuronal fibers. Among these algorithms, a novel approach is presented that successfully reconstructs fibers on phantom data as well as on human brain data. Further, a novel global classification approach is presented to cluster voxels according to their diffusion properties.
Die vorliegende Arbeit beschäftigt sich mit den Nutzenpotentialen des GeoPortal.rlp. Hierzu werden zwei empirische Studien erhoben. Die erste Studie ist eine Nutzerumfrage bezüglich des Portals. Die zweite Studie besteht aus Experteninterviews um potentielle Kooperationspartner zu identifizieren. Aus den Ergebnissen der beiden Studien werden Handlungsempfehlungen abgeleitet.
Identifying reusable legacy code able to implement SOA services is still an open research issue. This master thesis presents an approach to identify legacy code for service implementation based on dynamic analysis and the application of data mining techniques. rnrnAs part of the SOAMIG project, code execution traces were mapped to business processes. Due to the high amount of traces generated by dynamic analyses, the traces must be post-processed in order to provide useful information. rnrnFor this master thesis, two data mining techniques - cluster analysis and link analysis - were applied to the traces. First tests on a Java/Swing legacy system provided good results, compared to an expert- allocation of legacy code.