Image processing is commonly used to improve the signal:noise ratio and to allow more accurate analysis of biological imaging data. Image analysis is the extraction of data from images. Methods range from the simple, for instance plotting intensity profiles, to complex algorithms such as those used for object recognition and tracking over time. At the Gurdon Institute we routinely apply computational processing and analysis to obtain quantitative data from images taken using a range of microscope systems.
Fiji is an open-source image processing package based on ImageJ that can be used for almost any image processing or analysis task, and additional plugins can be added to enable new functions. Gurdon Institute members have published a number of papers that cite plugins written by the imaging facility. If you have questions about these plugins please email: email@example.com
To install a plugin, put the file in the ‘Fiji.app/Plugins/’ folder and restart Fiji or run ‘Help/Refresh’ menus. To access the relevant part of the filesystem under MacOS, Ctrl-click on the Fiji application icon to open the contextual menu and click ‘Show Package Contents’.
(click the link to download the plugin)
Object scan is an object mapping and analysis plugin that combines advanced functions with a user-friendly interface. Images are processed with a choice of feature enhancement algorithms, objects are identified by patch sampling to detect intensity edges based on the local energy gradient, and the generated two-dimensional masks are clustered in three dimensions to define the final object map for analysis. Analysis options include object intensity measurements, counts of foci inside primary objects, object intensity correlation, object co-localisation and lineage tracking. Scan parameters are set using a simple user interface and mapped objects are labelled for quick adjustment and testing of settings.
yeast_correlation calculates the Pearson Correlation Coefficient of pixel intensity values in a selected 3D region in two channels. The whole area and z-range can be used, or the analysis can be restricted to objects within the selected area mapped as continuous, smooth signal areas or as discrete, segmented regions.
Yeast_MitoMap maps mitochondria in a selected region and calculates the volume, surface area, and various shape descriptors that represent the overall distribution of signal in different ways, allowing quantification of mitochondrial morphology. The included readme file gives details of the pipeline including algorithms and formulae used.