DEFCoN-ImageJ

Description

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

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Vesicle segmentation method

Description

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

Description

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

Spot detector based on a 3D LoG filter

Description

Quote: "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."

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DNA MicroArray Image Processing Case Study

Description

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.

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FISH-quant

Description

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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2D spots counting using KNIME

Description

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

Description

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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FISH signals detection

Description

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. 

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CellProfiler Examples - Speckle Counting

Description

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.