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.

Spot Detector


Spot detector detects and counts spots, based on wavelet transform.

- Detects spots in noisy images 2D/3D.
- Depending on objective, spots can be nuclei, nucleus or cell
- Versatile input: sequence or batch of file.
- Detects spot in specific band/channel.
- Multi band labeling: automaticaly creates ROIs from one band and count in the same or an other band.
- Filters detection by size.
- Sort detection by ROIs
- Output data in XLS Excel files: number of detection by ROIs, and each detection location and size.
- Outputs withness image with ROIs and detection painted on it.
- Outputs binary detection image.
- Displays detections
- Displays tags

logo spot detector

DNA MicroArray Image Processing Case Study


In this case study, MATLAB, the Image Processing and Signal Processing toolboxes were used to determine the green intensities from a small portion of a microarray image containing 4,800 spots. A 10x10 pattern of spots was detected by averaging rows and columns to produce horizontal and vertical profiles. Periodicity was determined automatically by autocorrelation and used to form an optimal length filter for morphological background removal. A rectangular grid of bounding boxes was defined. Each spot was individually addressed and segmented by thresholding to form a mask. The mask was used to isolate each spot from surrounding background. Individual spot intensity was determined by integrating pixel intensities. Finally, integrated intensities were tabulated and saved to a data file for subsequent statistical analysis to determine which genes matter most.



Matlab toolbox to analyze single molecule mRNA FISH data. Allows counting the number of mature and nascent transcripts in 3D images. See 2513. Following toolboxes are required: - Optimization toolbox - Statistics toolbox - Image processing toolbox - (Optional) Parallel processing toolbox


Input data type: 3D image

Output data type: CSV

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Fluorescence in situ hybridization (FISH) is used to study the organization and the positioning of specific DNA sequences within the cell nucleus. Analyzing the data from FISH images is a tedious process that invokes an element of subjectivity. Automated FISH image analysis offers savings in time as well as gaining the benefit of objective data analysis. While several FISH image analysis software tools have been developed, they often use a threshold-based segmentation algorithm for nucleus extraction. As fluorescence signal intensities can vary significantly from experiment to experiment, from cell to cell, and within a cell, threshold based segmentation is inflexible and often insufficient for automatic image analysis, leading to additional manual extraction and potential subjective bias. To overcome these problems, we developed a graphical software tool called FISH Finder to automatically analyze FISH images that vary significantly. By posing the nucleus extraction as a classification problem, compound Bayesian Classifier is employed so that contextual information is utilized, resulting in reliable classification and boundary extraction. This makes it possible to analyze FISH images efficiently and objectively without adjustment of input parameters.

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2D spots counting using KNIME


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)

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Batch spot detection with custom output


Download the protocol,use and modify in Icy. It permits to detect spot with wavelet spot detector block. Input : loop on a folder Outputs: excel, binary, and detection screenshot

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need a thumbnail



Oufti (previously named MicrobeTracker) is a MATLAB application / suite of tools for analysing fluorescent spots inside microbes. MicrobeTracker can identify cell outlines and fluorescent foci, and generate plots and statistics based on positions and intensity (kymographs, histograms etc.) The MATLAB code is easy to modify and extend to add additional plots and statistics: see e.g. Lesterlin et al. (2014).

The Outfi Forum is quite active.