Authors:
Paul Klemm
1
;
Sylvia Glaßer
1
;
Kai Lawonn
1
;
Marko Rak
1
;
Henry Völzke
2
;
Katrin Hegenscheid
2
and
Bernhard Preim
1
Affiliations:
1
University of Magdeburg, Germany
;
2
University of Greifswald, Germany
Keyword(s):
Epidemiology, Interactive Visual Analysis, Classification, Multi-Modal Data.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
Databases and Visualization, Visual Data Mining
;
General Data Visualization
;
Large Data Visualization
;
Visual Data Analysis and Knowledge Discovery
;
Visualization Applications
Abstract:
Epidemiology aims to provide insight into disease causations. Hence, subject groups (cohorts) are analyzed
to correlate the subjects’ varying lifestyles, their medical properties and diseases. Recently, these cohort
studies comprise medical image data. We assess potential relations between image-derived variables of the
lumbar spine with lower back pain in a cross-sectional study. Therefore, an Interactive Visual Analysis (IVA)
framework was created and tested with 2,540 segmented lumbar spine data sets. The segmentation results are
evaluated and quantified by employing shape-describing variables, such as spine canal curvature and torsion.
We analyze mutual dependencies among shape-describing variables and non-image variables, e.g., pain indicators.
Therefore, we automatically train a decision tree classifier for each non-image variable. We provide
an IVA technique to compare classifiers with a decision tree quality plot. As a first result, we conclude that
image-based variables are
only sufficient to describe lifestyle factors within the data. A correlation between
lumbar spine shape and lower back pain could not be found with the automatically trained classifiers. However,
the presented approach is a valuable extension for the IVA of epidemiological data. Hence, relations
between non-image variables were successfully detected and described.
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