Spot detection

Particle detection is based on "Analyze Particles" in ImageJ. It probably could also be used in spot detection, not limited to centromere.

This macro is described in Bodor et al. (2012). The macro recognizes centromere or kinetochore foci in Delta Vision or TIFF images and determines their centroid position. Fluorescent intensities are then measured for each centromere by placing a small box around the centroid position of the centromere. The peak intensity value within the box is corrected for local background by subtraction of the minimum pixel value. This process results in an accurate measurement of large numbers of centromere or kinetochore-specific signals.

Following papers uses CRaQ (picked up, maybe more):

• Fachinetti et al. (2017), Developmental Cell 40, 104–113,
• Guo et al. (2017) Nature Communications volume 8, Article number: 15775 (2017) doi:10.1038/ncomms15775
• Lgosdon et. al. (2015) J Cell Biol Mar 2015, 208 (5) 521-531; DOI: 10.1083/jcb.201412011
• Bodor et al. (2014), eLife. 2014; 3: e02137

This workflow estimates (densely distributed) object counts by the density of objects in the image without performing segmentation or object detection. Current version only works for 2D images of roundish objects with similar sizes on relatively homogeneous background. Users should provide a few labels of background and objects (especially on clustered objects), and the tool predicts the density of objects on the entire image. Counting is then estimated by integrating the density values on the whole image or specified rectangular regions of interests.

A set of classes and functions which can be used by plugins performing spot detection and spot tracking.

An ImageJ plugin for evaluate intra-nuclear 3D cross-distances between fluorescent spots in multi-channel images