In the last years, bioimaging has turned from qualitative measurements towards a high-throughput and highcontent
modality, providing multiple variables for each biological sample analyzed. We present a system which
combines machine learning based semantic image annotation and visual data mining to analyze such new multivariate
bioimage data. Machine learning is employed for automatic semantic annotation of regions of interest.
The annotation is the prerequisite for a biological object-oriented exploration of the feature space derived from
the image variables. With the aid of visual data mining, the obtained data can be explored simultaneously in
the image as well as in the feature domain. Especially when little is known of the underlying data, for example
in the case of exploring the effects of a drug treatment, visual data mining can greatly aid the process of
data evaluation. We demonstrate how our system is used for image evaluation to obtain information relevant
to diabetes study and screening of new anti-diabetes treatments. Cells of the Islet of Langerhans and whole
pancreas in pancreas tissue samples are annotated and object specific molecular features are extracted from
aligned multichannel fluorescence images. These are interactively evaluated for cell type classification in order
to determine the cell number and mass. Only few parameters need to be specified which makes it usable also for
non computer experts and allows for high-throughput analysis.