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

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Herold, Julia, Abouna, Sylvie, Zhou, Luxian, Pelengaris, Stella, Epstein, D. B. A., Khan, Michael and Nattkemper, Tim W. (2009) A way toward analyzing high-content bioimage data by means of semantic annotation and visual data mining. In: Medical Imaging 2009: Image Processing, Lake Buena Vista, FL, USA, 8th Feb, 2009. Published in: Proceedings of SPIE - International Society for Optical Engineering, Vol.7259 Article No.72591Q. ISSN 0277-786X. doi:10.1117/12.811710

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Official URL: http://dx.doi.org/10.1117/12.811710

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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.

Item Type: Conference Item (Poster)
Subjects: Q Science > Q Science (General)
Divisions: Faculty of Science > Life Sciences (2010- )
Faculty of Science > Mathematics
Journal or Publication Title: Proceedings of SPIE - International Society for Optical Engineering
Publisher: S P I E - International Society for Optical Engineering
ISSN: 0277-786X
Book Title: Proceedings of SPIE
Official Date: 2009
Dates:
DateEvent
2009Published
Volume: Vol.7259
Page Range: Article No.72591Q
DOI: 10.1117/12.811710
Status: Not Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Conference Paper Type: Poster
Title of Event: Medical Imaging 2009: Image Processing
Type of Event: Conference
Location of Event: Lake Buena Vista, FL, USA
Date(s) of Event: 8th Feb, 2009

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