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Heimo Wolinski, PhD
University Graz
Senior Scientist
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8010 Graz, Austria
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High Content Screening (HCS) Profiler v1.0: a scripting-customized software extension for amira visualization platforms


Amira is a modular visualization, data analysis and geometry reconstruction software system. The software package contains a large number of modules for visualization and analysis especially of multi-dimensional image data. Amira is based on sophisticated industry standard libraries (OpenGL, Open InventorTM, QtTM, Tcl) and is being used in different scientific fields such as medicine, biology or physics (

One outstanding feature of amira is that most of the functionality of the software can be accessed via "scripting". Generally, a script is a user-defined set of specific commands which can be processed by qualified software systems. In this context, amira includes an interpreter for the Tool Command Language (Tcl). Tcl is a simple scripting language and easy to learn. Therefore, scripting-customized software tools can be created by software users without extensive programming knowledge. A large numer of Tcl related web resources are available e.g.:
Large resource including tutorials.
Documentations, articels etc.
Tcl reference manual.

In contrast to simple macros a Tcl based script provides more flexible operations. Routines can be implemented such as to examine information or to take actions in certain situations. In addition, amira scripting allows the implementation of own user-interface elements into the basic amira platform. In this way, powerful software solutions for individual applications can be created.


HCS Profiler is a scripting-customized software extension for amira visualization platforms. The software provides features for automated filtering, visualization and quantification of multi-channel multi-dimensional image data e.g. as acquired using the imaging-based high-content screening method developed in our lab.

The image processing pipeline includes:

- Assignment of ORF names to acquired data sets.
- Filtering of 3D fluroescence data (e.g. Gaussian filtering; Median filtering).
- Projection of 3D fluroescence and transmission data; images include unique identifier.
- Quantification of acquired fluorescence images (histograms, pixel statistics).
- Segmentation of 3D fluorescence data (e.g. thresholding, factorization, moments, entropy (auto binarization).
- Projection of 3D binary data.
- Quantification of extracted 3D objects (45 parameters for each object of a 3D data set; e.g. volume, surface area, mean intensity, different shape parameters). Histograms based on segmented image objects.

Due to the highly modular architecture of amira additional functions can be implemented easily. In this context, the used amira package provides a large number of image processing and image quantification routines or allows the implementation of routines written using external software such as Matlab. The add-on requires amira 4 including quantification package.

HCS Profiler v1.0 user interface.

DOWNLOAD HCS Profiler V1.0 demo movie:

>>> HCS Profiler v1.0 demo movie (*.gif, 5.2 MB, 00:03:26) <<<

Please note: the demo movie shows only one possible way to process the data. The image processing pipeline can be modified easily and be adapted for specific approaches. For instance, for the high-content screening method developed in our lab histograms are computed for segmented image objects and not for entire images as shown in the demo movie.

The movie can be viewed using QuickTime Player or using any web browser (simply click on the link using the left mouse button)!

1. Wolinski H, Petrovic U, Mattiazzi M, Petschnigg J, Heise B, Natter K, Kohlwein SD (2009). Imaging-based live cell yeast screen identifies novel factors involved in peroxisome assembly. J Proteome Res. 2009 Jan;8(1):20-7.
2.Lamprecht, M. R.; Sabatini, D. M.; Carpenter, A. E., CellProfiler: free, versatile software for automated biological image analysis. Biotechniques 2007, 42, (1), 71-5.
3.Vokes, M. S.; Carpenter, A. E., Using CellProfiler for automatic identification and measurement of biological objects in images. Curr Protoc Mol Biol 2008, Chapter 14, Unit 14 17.
4.Chernomoretz, A.; Bush, A.; Yu, R.; Gordon, A.; Colman-Lerner, A., Using Cell-ID 1.4 with R for microscope-based cytometry. Curr Protoc Mol Biol 2008, Chapter 14, Unit 14 18.
5.Chen, X.; Velliste, M.; Murphy, R. F., Automated interpretation of subcellular patterns in fluorescence microscope images for location proteomics. Cytometry A 2006, 69, (7), 631-40.
6.Chen, X.; Murphy, R. F., Automated interpretation of protein subcellular location patterns. Int Rev Cytol 2006, 249, 193-227.

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