Free and open source

The macro segments and classifies human spermatozoids nuclei (DAPI) based on the number of FISH signals (spots) they contain. It reports the percentage of occurrences of user defined classes (combinations of spot multiplicity in the FISH channels) as well as the position (point selections) of the detected nuclei falling in these classes. The input image should be an hyperstack with 4 channels: DAPI (first channel) and three FISH channels. The images are typically obtained as a maximum intensity projection of few channels (confocal) or a single z slice acquisition (widefield).

Example image available in the linked page. 

Quote:

This pipeline shows how to identify smaller objects (foci) within larger objects (nuclei) and how to use the Relate module to establish a relationship between the two as well as perform per-object aggregate measurements (such as number of foci per nucleus). Sample images are included in the download package.

The quantification is explained in detail in chapter 8 "Cell Polarity - Focal Adhesion and Actin Dynamics in Migrating Cells" in "Bioimage Data Analysis Book" downloadable from here.

For codes and sample images, download the zipped archive (linked under "Download").

Quote:

Measuring the colocalization between fluorescently labeled molecules is a widely used approach to measure the degree of spatial coincidence and potential interactions among subcellular species (e.g., proteins). This example shows how the object identification and RelateObjects modules are used to measure the degree of overlap between two fluorescent channels. Sample image is included in the download package.

Microtubule end tracking in live cell fluorescent images of Drosophila oocyte involves overcoming the following challenges, which can be tackled by a series of preprocessing steps and tracking described in Parton et al (2011)

• illumination flicker & photobleaching: suppress by normalising intensities, e.g. using Image->Adjust->Bleach Correction in Fiji/ImageJ
• uneven illumination: Fourier bandpass filtering (e.g. Process->FFT->Bandpass Filter) preserves features within a selected size range
• high background / poor contrast: foreground filter, e.g. Temporal Median filter
• tracking: e.g. TrackMate in Fiji/ImageJ (segmentation using DoG detector)