A way toward analyzing high-content bioimage data by means of semantic annotation and visual data mining

Julia Herold; Sylvie Abouna; Luxian Zhou; Stella Pelengaris; David B. A. Epstein; Michael Khan; Tim W. Nattkemper

doi:10.1117/12.811710

27 March 2009 A way toward analyzing high-content bioimage data by means of semantic annotation and visual data mining

Julia Herold, Sylvie Abouna, Luxian Zhou, Stella Pelengaris, David B. A. Epstein, Michael Khan, Tim W. Nattkemper

Proceedings Volume 7259, Medical Imaging 2009: Image Processing; 72591Q (2009) https://doi.org/10.1117/12.811710
Event: SPIE Medical Imaging, 2009, Lake Buena Vista (Orlando Area), Florida, United States

Abstract

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.

Citation Download Citation

Julia Herold, Sylvie Abouna, Luxian Zhou, Stella Pelengaris, David B. A. Epstein, Michael Khan, and Tim W. Nattkemper "A way toward analyzing high-content bioimage data by means of semantic annotation and visual data mining", Proc. SPIE 7259, Medical Imaging 2009: Image Processing, 72591Q (27 March 2009); https://doi.org/10.1117/12.811710

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