The tool detects and and counts the spots (or blobs) in an image. It has been created for the counting of bacteria colonies in in Petri-dishes. It separates the spots into two populations and counts each population individually. The populations are separated by the area of the spots. The tool uses expectation maximisation clustering [1] from the weka software [2].

To install the tool save the files Count_Spot_Populations_Tool.ijm and results_table-clusterer.py into the folder macros/toolsets of your FIJI installation.

Select the "3D_nuclei_clustering" toolset from the >> button of the ImageJ launcher.

• the first button opens this help page
• the c-button starts counting and clustering of the spots on the active image
• the d-button runs a DoG-filter on the active image
• the r-button runs the clustering by area on a results table
• the p-button plots a histogram and the distributions of the spot areas
• the e-button opens an extras menu, from which example images can be downloaded

Open an input image. The tool will detect dark spots on a bright background. If necessary invert the contrast of the image (Edit>Invert) before running the tool. Set the options for the detection of the Petri-dish and the spots (see below). Make sure that Area is selected in Analyze>Set Measurements.... Press the c-button to run the analysis.

The clustering can also be run on a results table, using the r-button. The results table must contain the column Area. The plotting of the area-histogram and distributions with the p-button is also possible in this case.

Right-click on the c-button to open the options dialog.

Petri-dish margin

Allows to exclude a smaller or bigger part that corresponds to the border of the Petri-dish. Set to zero to use the whole Petri-dish or the whole image

threshold method

The auto-thresholding method used for the spot-segmentation.

min. circularity

Allows to filter spots by their circularity.

fit ellipse

If selected an ellipse will be fit to each segmented spots.

contrast

Select the contrast of the image: auto, normal or inverted.

min. diameter of spots

The minimum diameter of the spots

max. diameter of spots

The maximum diameter of the spots

color cluster one

the color in which the distribution of the smaller areas is displayed

color cluster two

the color in which the distribution of the bigger areas is displayed

color histogram

the color in which the histogram of the areas is displayed

histogram bin width

the bin width of the area-histogram

distribution line width

the line width for the distributions in the plot

• MRI_Create_Synthetic_Spots_Tool

1. Yong Gyu Jung, Min Soo Kang & Jun Heo (2014) Clustering performance comparison using K-means and expectation maximization algorithms, Biotechnology & Biotechnological Equipment, 28:sup1, S44-S48, DOI: 10.1080/13102818.2014.949045

2. Eibe Frank, Mark A. Hall, and Ian H. Witten (2016). The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques", Morgan Kaufmann, Fourth Edition, 2016.