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BMCBI
2004
208views more  BMCBI 2004»
13 years 4 months ago
Hybrid clustering for microarray image analysis combining intensity and shape features
Background: Image analysis is the first crucial step to obtain reliable results from microarray experiments. First, areas in the image belonging to single spots have to be identif...
Jörg Rahnenführer, Daniel Bozinov
BMCBI
2007
207views more  BMCBI 2007»
13 years 4 months ago
Analyzing in situ gene expression in the mouse brain with image registration, feature extraction and block clustering
Background: Many important high throughput projects use in situ hybridization and may require the analysis of images of spatial cross sections of organisms taken with cellular lev...
Manjunatha Jagalur, Chris Pal, Erik G. Learned-Mil...
ECCV
2006
Springer
13 years 8 months ago
SIFT and Shape Context for Feature-Based Nonlinear Registration of Thoracic CT Images
Nonlinear image registration is a prerequisite for various medical image analysis applications. Many data acquisition protocols suffer from problems due to breathing motion which h...
Martin Urschler, Joachim Bauer, Hendrik Ditt, Hors...
BMCBI
2006
130views more  BMCBI 2006»
13 years 4 months ago
CARMA: A platform for analyzing microarray datasets that incorporate replicate measures
Background: The incorporation of statistical models that account for experimental variability provides a necessary framework for the interpretation of microarray data. A robust ex...
Kevin A. Greer, Matthew R. McReynolds, Heddwen L. ...
BIOINFORMATICS
2005
93views more  BIOINFORMATICS 2005»
13 years 4 months ago
Donuts, scratches and blanks: robust model-based segmentation of microarray images
Inner holes, artifacts and blank spots are common in microarray images, but current image analysis methods do not pay them enough attention. We propose a new robust model-based me...
Qunhua Li, Chris Fraley, Roger Eugene Bumgarner, K...