Seed point detection

GaussFit OnSpot



GaussFit_OnSpot is an ImageJ plugin for fitting Gaussian profiles onto selected positions in diffraction-limited images (e.g. single molecules, protein clusters, vesicles, or stars).

The plugin performs a function fit in regions of interest (ROI) around spots marked by point selections in grayscale images. Single or multiple spots can be either selected manually with the Multi-point tool or automatically with the Find Maxima function.

There is a PDF with more information, and also an example image.

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Spots colocalization (ComDet)


Quote " finding and/or analyzing colocalization of bright intensity spots (cells, particles, vesicles, comets, dots, etc) in images with heterogeneous background (microscopy, astronomy, engineering, etc). "

Uses Gaussian-Mexican hat convolution for preprocessing.



h-Dome transformation, useful for spot detection.

Jython code example:

from de.unihalle.informatik.MiToBo.core.datatypes.images import MTBImage
from de.unihalle.informatik.MiToBo.morphology import HDomeTransform3D
from ij import IJ

imp = IJ.getImage()
mtb = MTBImage.createMTBImage( imp.duplicate() )
hdome = HDomeTransform3D(mtb, 10.0)
mtbdone = hdome.getResultImage()
imp2 = mtbdone.getImagePlus()



This is a classical workflow for spot detection or blob like structures (vesicules, melanosomes,...)

Step 1 Laplacian of Gaussian to enhance spots . Paraeters= radius, about the average spot radius

Step 2 Detect minima (using Find Maxima with light background option to get minima). Parameter : Tolerance to Noise: to be tested, hard to predict. About the height of the enhanced feautures peaks

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spot detection

Interactive watershed


The interactive Watershed Fiji plugin provides an interactive way to explore local maxima and threshold values while a resulting label map is updated on the fly.

After the user has found a reliable parameter configuration, it is possible to apply the same parameters to other images in a headless mode, for example via ImageJ macro scripting.



Acquiarium is open source software (GPL) for carrying out the common pipeline of many spatial cell studies using fluorescence microscopy. It addresses image capture, raw image correction, image segmentation, quantification of segmented objects and their spatial arrangement, volume rendering, and statistical evaluation.

It is focused on quantification of spatial properties of many objects and their mutual spatial relations in a collection of many 3D images. It can be used for analysis of a collection of 2D images or time lapse series of 2D or 3D images as well. It has a modular design and is extensible via plug-ins. It is a stand-alone, easy to install application written in C++ language. The GUI is written using cross-platform wxWidgets library.

Acquiarium functionalities diagram



The freely available software module below is a 3D LoG filter. It applies a LoG (Laplacian of Gaussian or Mexican Hat) filter to a 2D image or to 3D volume. Here, we have a fast implementation. It is a perfect tool to enhance spots, like spherical particles, in noisy images. This module is easy to tune, only by selecting the standard deviations in X, Y and Z directions.

IJ Macro command example

run("LoG 3D", "sigmax=1 sigmay=1 sigmaz=13 displaykernel=0 volume=1");



An ImageJ plugin for DEFCoN, the fluorescence spot counter based on fully convolutional neural networks

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ATLAS Vesicle segmentation method


Part of ATLAS software

Comment / Instructions: 

You can upload your image at the Mobyle@SERPICO portal and download the result. The workflow is only available online, i.e. no download possible.

GDSC plugins


Quote: "The GDSC ImageJ plugins are a collection of analysis programs for microscopy images including colocalisation analysis and peak finding (FindFoci)."

Many types of analysis besides simply finding foci detection (spot detection) is bundled in this plugin. One prominent function is "FindFoci Optimizer". This allows feeding images with spot annotation by the user (multi-point selection tool) and scans through various parameter combinations to find the best parameter set that gives the results similar to the annotation. This is almost like machine learning... but with well-established parameter types that allows you to fully understand what is going on.