Matlab

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.

A deconvolution component applicable to confocal and STED microscopy. The MATLAB function fo this package implements the SGP method for n-dimensional object deblurring with the option of boundary effects removal. Although this is a preliminary version, results seem to be good from their paper (Zanella et al 2013).

CellDetector can detect cells (or other objects) in microscopy images such as histopathology, fluorescence, phase contrast, bright field, etc. It uses a machine learning-based method where a cell model is learned from simple dot annotations on a few images for training and predict on test sets. The installation requires some efforts but the instruction is well explained. Training parameters should be tuned for different datasets, but the default settings could be a good starting point.

The overall colors seen in H&E stained slides can vary widely, influenced by factors such as the precise stains and scanner used. This MATLAB function implements the color normalization strategy described in Macenko et al (2009) in order to match stain colors in an image more closely to 'reference' stains. This may help when comparing images visually, or when applying an automated analysis algorithm.

The function may also be useful to understand the functioning of the color deconvolution described in Ruifork and Johnston (2001).