Computational Framework for Biotechnological Research

High-throughput image analysis

Computational technologies are used to accelerate or fully automate the processing, quantification and analysis of large amounts of high-information-content biomedical imagery. Modern image analysis systems augment an observer's ability to make measurements from a large or complex set of images, by improving accuracy, objectivity, or speed. A fully developed analysis system may completely replace the observer. Although these systems are not unique to biomedical imagery, biomedical imaging is becoming more important for both diagnostics and research. Some examples are:

High-throughput and high-fidelity quantification and sub-cellular localization (high-content screening, cytohistopathology).
Morphometrics
Clinical image analysis and visualization.
Determining the real-time air-flow patterns in breathing lungs of living animals.
Quantifying occlusion size in real-time imagery from the development of and recovery during arterial injury.
Making behavioural observations from extended video recordings of laboratory animals.
Infrared measurements for metabolic activity determination.
Inferring clone overlaps in DNA mapping, e.g. the Sulston score.
Computational Framework for Biotechnological Research
 
 
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