Semi-automated

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.

..A toolbox for high-throughput screening of image-based Caenorhabditis elegans phenotypes. The image analysis algorithms measure morphological phenotypes in individual worms and are effective for a variety of assays and imaging systems. This WormToolbox is available through the open-source CellProfiler project and enables objective scoring of whole-worm high-throughput image-based assays of C. elegans for the study of diverse biological pathways that are relevant to human disease.

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.

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

The sample dataset (available in the github repository) contains 2-Photon images of neurons. The neurons were imaged in 3D at two time frames.To allow measuring significant differences in cell volume, the time gap between the frames is large (ca. 30 min) and the animal was removed in the waiting phase. For this reason, there is a considerable shift in sample position between the frames that has to be corrected before cell detection and tracking.

The workflow consists of following steps:

1. Import of single tiff slices [imageJ macro]

2. Organizing the data in a 4D time series with 2 time frames [imageJ macro]

3. Correction of shift between the time frames by rigid registration [imagJ macro]

4. Bleaching correction [imageJ macro]

5. Export of preprocessed image data in ics/ids format [imageJ macro]

6. Import of ics/ids data to Imaris [Imaris]

7. Cell object detection as "Imaris Surface Object" [Imaris]

8. Tracking cell objects over time [Imaris]

9. Split Tracks (use Imaris XT extension "Split Tracks") to generate single cell objects [Imaris]

10. Export the statistics: Select the complete folder, go to the statistics tab and use ‚Full Export’ [Imaris]

The preprocessing macro can be referenced here.

The sample images were acquired by Cordula Ulbrich (Petzold Group at German Center of Neurodegenerative Disesases (DZNE), Bonn, Germany).

Input data type: tiff

Output data type: data table

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).

- Destriping (the problem that motivated us to develop these ideas). 

- Deconvolution (even though most users won't be able to use this feature).

- Cartoon + texture decomposition which might be useful to compress images, analyse textures or simplify segmentation like tasks.