TY  - THES
A1  - Lichtenberg, Nils
T1  - Abstraction of Bio-Medical Surface Data for Enhanced Comprehension and Analysis
N2  - Bio-medical data comes in various shapes and with different representations.
Domain experts use such data for analysis or diagnosis,
during research or clinical applications. As the opportunities to obtain
or to simulate bio-medical data become more complex and productive,
the experts face the problem of data overflow. Providing a
reduced, uncluttered representation of data, that maintains the data’s
features of interest falls into the area of Data Abstraction. Via abstraction,
undesired features are filtered out to give space - concerning the
cognitive and visual load of the viewer - to more interesting features,
which are therefore accentuated. To address this challenge, the dissertation
at hand will investigate methods that deal with Data Abstraction
in the fields of liver vasculature, molecular and cardiac visualization.
Advanced visualization techniques will be applied for this purpose.
This usually requires some pre-processing of the data, which will also
be covered by this work. Data Abstraction itself can be implemented
in various ways. The morphology of a surface may be maintained,
while abstracting its visual cues. Alternatively, the morphology may
be changed to a more comprehensive and tangible representation.
Further, spatial or temporal dimensions of a complex data set may
be projected to a lower space in order to facilitate processing of the
data. This thesis will tackle these challenges and therefore provide an
overview of Data Abstraction in the bio-medical field, and associated
challenges, opportunities and solutions.
Y1  - 2020
UR  - https://kola.opus.hbz-nrw.de/frontdoor/index/index/docId/2033
UR  - https://nbn-resolving.org/urn:nbn:de:kola-20332
ER  -