Automated

The software is designed for pathologists. Image analysis protocols are built from graphical user interfaces; there is no need for programming experience or an extensive training program. Cloud and deployed solutions are available. Visiopharm can be employed to develop workflows (apps) for the user. Modular structure with multiple packages: VisiomorphDP™ TissuemorphDP™ Arrayimager™ Tissuealign™ Visiomorph™ Tissuemorph™ Microimager™ Fluoimager™

An easy to use, image analysis software package that enables rapid exploration and interpretation of microscopy data.

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.

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…)

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Perfect for the beginner, this demo illustrates simple object detection (segmentation, feature extraction), measurement, and filtering. Requires the Image Processing Toolbox (IPT) because it demonstrates some functions supplied by that toolbox, plus it uses the "coins" demo image supplied with that toolbox. If you have the IPT (you can check by typing ver on the command line), you should be able to run this demo code simply by copying and pasting this code into a new editor window, and then clicking the green "run" triangle on the toolbar. First finds all the objects, then filters results to pick out objects of certain sizes. The basic concepts of thresholding, labeling, and regionprops are demonstrated with a simple example.

It's a good tutorial for those users new to MATLAB's image processing capabilities to learn on, before they go on to more sophisticated algorithms.