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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. doi:10.1117/12.811710 ISSN 0277-786X.
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Official URL: http://dx.doi.org/10.1117/12.811710
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) | ||||
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Subjects: | Q Science > Q Science (General) | ||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Life Sciences (2010- ) Faculty of Science, Engineering and Medicine > Science > Mathematics |
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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: |
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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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