Contents

Measurement of kymograph generally deals with slanted streaks in kymograph to measure velocity of movements. This is a pretty much manual procedure that needs hands-on work. Kymoquant analyzes kymograph by treating patterns appearing in kymograph as texture: for a specified area in kymograph, it detects the most likely orientation of streaks and outputs this result as velocity. This workflow was created to ease the situation when it is difficult to find major direction of streaks only with eyes.

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.

Rigid registration of time series in 3D. A video tutorial is available (be careful of sounds, the video automatically starts!): [Sample Drift Correction Following 4D Confocal Time-lapse Imaging](http://www.jove.com/video/51086/sample-drift-correction-following-4d-co…)

Quote

Normalize the orientation of the images of the Zebrafish embryos.

In the documentation webpage, the aim of the workflow is to normalize the orientation of the images of the Zebrafish embryos, find the point of injection of tumor cells and measure the distribution of Cy3 stained tumor foci.

ImageJ macro implementation of the Workflow described in Ghotra et al (2012). Note that currently only the angle and orientation normalization is implemented in this version.

Sample images are linked in the documentation webpage. 

This Matlab code demonstrates an edge-based active contour model as an application of the Distance Regularized Level Set Evolution (DRLSE) formulation.

This toolbox is a unique collection of workflow templates for high-throughput screening. 4 different workflow templates are presented in the documentation linked below.

idTracker is a videotracking software that keeps the correct identity of each individual during the whole video. It works for many animal species including mice, insects (Drosophila, ants) and fish (zebrafish, medaka, stickleback). idTracker distinguishes animals even when humans cannot, such as for size-matched siblings, and reidentifies animals after they temporarily disappear from view or across different videos. It is robust, easy to use and general. Technique details and analyses of several applications are described in Pérez-Escudero et al (2014).

ImageJ plugin to analyze changes in vessel diameters, described in Fernández er al (2014). More specifically the paper describes the measurement of isolated retinal arterioles (ca 50 micrometer diameter) but can be used for diameter measurements of similar vessel structures.

Using a text file containing 3D point coordinates as reference pairs, 3D image stack is transformed.

The workflow measures the growth of cells in 3D, combining an ImageJ macro for preprocessing and successive tracking using Imaris.  

Variational algorithms to remove stationary noise. Application to microscopy imaging. This plugin allows to denoise images degraded with stationary noise. Stationary noise can be seen as a generalization of the standard white noise. Typical applications of this plugin are:

- Standard white noise denoising using a total variation and fidelity term minimization. Even though total variation denoising is not the state of the art (regarding SNR improvement), it may be very valuable for further tasks such as image seg- mentation).

An automated MATLAB tool for segmentation of surface stained cells

DBSCAN (Density-based spatial clustering of applications with noise) performs multi-dimensional clustering based on the local density of points. This plugin is implemented for 2-3 dimensions.

Two workflows are proposed here:

one based on fiducials, the other one on cross-correlation.

A clear tutorial on how to write a MATLAB script to segment clustered cells.

The full script is downloadable near the bottom of the article.