Integrating semantic annotation and information visualization for the analysis of multichannel fluorescence micrographs from pancreatic tissue
Herold, Julia, Zhou, Luxian, Abouna, Sylvie, Pelengaris, Stella, Epstein, David, Khan, Michael and Nattkemper, Tim W. (2010) Integrating semantic annotation and information visualization for the analysis of multichannel fluorescence micrographs from pancreatic tissue. In: 8th IEEE International Conference on Bioinformatics and Bioengineering, Athens, Greece, October 08-10, 2008. Published in: Computerized Medical Imaging and Graphics, No.34 (No.6). pp. 446-452.Full text not available from this repository.
Official URL: http://dx.doi.org/10.1016/j.compmedimag.2009.10.00...
The challenging problem of computational bioimage analysis receives growing attention from life sciences. Fluorescence microscopy is capable of simultaneously visualizing multiple molecules by staining with different fluorescent dyes. In the analysis of the result multichannel images, segmentation of ROIs resembles only a first step which must be followed by a second step towards the analysis of the ROI's signals in the different channels. In this paper we present a system that combines image segmentation and information visualization principles for an integrated analysis of fluorescence micrographs of tissue samples. The analysis aims at the detection and annotation of cells of the Islets of Langerhans and the whole pancreas, which is of great importance in diabetes studies and in the search for new anti-diabetes treatments. The system operates with two modules. The automatic annotation module applies supervised machine learning for cell detection and segmentation. The second information visualization module can be used for an interactive classification and visualization of cell types following the link-and-brush principle for filtering. We can compare the results obtained with our system with results obtained manually by an expert, who evaluated a set of example images three times to account for his intra-observer variance. The comparison shows that using our system the images can be evaluated with high accuracy which allows a considerable speed up of the time-consuming evaluation process. (C) 2009 Elsevier Ltd. All rights reserved.
|Item Type:||Conference Item (UNSPECIFIED)|
|Divisions:||Faculty of Science > Life Sciences (2010- ) > Biological Sciences ( -2010)
Faculty of Science > Mathematics
Faculty of Medicine > Warwick Medical School
|Journal or Publication Title:||Computerized Medical Imaging and Graphics|
|Number of Pages:||7|
|Page Range:||pp. 446-452|
|Access rights to Published version:||Restricted or Subscription Access|
|Title of Event:||8th IEEE International Conference on Bioinformatics and Bioengineering|
|Type of Event:||Conference|
|Location of Event:||Athens, Greece|
|Date(s) of Event:||October 08-10, 2008|
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