mitochondria

This plugin tags all pixel/voxels in a skeleton image and then counts all its junctions, triple and quadruple points and branches, and measures their average and maximum length.

Tags are shown in a new window displaying every tag in a different color. You can find it under [Plugins>Skeleton>Analyze Skeleton (2D/3D)]. See Skeletonize3D for an example of how to produce skeleton images.

The voxels are classified into three different categories depending on their 26 neighbors: - End-point voxels: if they have less than 2 neighbors. - Junction voxels: if they have more than 2 neighbors. - Slab voxels: if they have exactly 2 neighbors.

End-point voxels are displayed in blue, slab voxels in orange and junction voxels in purple.

Notice here that, following this notation, the number of junction voxels can be different from the number of actual junctions since some junction voxels can be neighbors of each other.

Output data type: table result, image of the skeleton

ImageJ native "Skeletonize" implementation. - works only with 8-bit binary image. A faster implementation is available as a plugin Skeletonize3D written by Ignacio Arganda-Carreras. Pros of this plugin is summarized here.

These two KNIME workflow solutions are similar: first one detects nuclei and spots inside the nuclei without taking care of surrounding regions, i.e. mitochondria. The second one provides the full solution including spots in mitochondria.

see section 2.4 for KNIME workflow. Section 2.3 is also available, using Fiji. 

Sample image: hela-cells.tif (674k x 3)

The original paper describes a method to analyze mitochondrial morphology in 2D and 3D.

For post-processing analysis, use Analyze Skeleton plugin written by the same author.