Collection

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.

An automated MATLAB tool for segmentation of surface stained cells

The linked webpage presents a collection of ImageJ macros for Intelligent Imaging (Feedback to microscope system for the secondary scan). 

An ImageJ macro able to control some microscopes (Micro-manager or Leica CAM controlled) to acquire high resolution images of only some structures (e.g. isolated cells) or events (e.g. mitosis) within a sample. The scan is sequenced as a primary (low resolution monitoring) scan and a secondary (high resolution, multi-dimensional) scan.

The rapidSTORM project is an open source evaluation tool that provides fast and highly configurable data processing for single-molecule localization microscopy such as dSTORM. It provides both two-dimensional and three-dimensional, multi-color data analysis as well as a wide range of filtering and image generation capabilities. The general operation of rapidSTORM is described in Wolter et al (2012).

Quote:

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.