User Manual available online

Finally, the very first version of user manual is available online now under the following link.

eResearch Australasia 2013 presentations

At the eResearch Australasia 2013 conference we carried out half a day workshop on Image Analysis and Processing in the Clouds using Scalable eResearch Workflow-Based System. 

Our presentations are now available online:

• Introduction, here
• Distributed Scientific Computing in Clouds, here
• Galaxy Introduction, here
• Cellular Imaging Tools on NeCTAR Cloud, here
• Cellular Imaging: Neuronal Complexity Workflow, here
• Medical Imaging, here
• CT Reconstruction Workflow, here

We are officially online

The Cloud-based Image Analysis and Processing Toolbox project provides access to existing biomedical image processing and analysis tools via remote user‐interface using the NeCTAR cloud.

If you are interested to use our tools, please contact us.

Input Validation in Galaxy

Input Validation in Galaxy

Just wanted to share, Galaxy has nice input validation features, i.e. the following code generates the following error:

And if more complicated validation is required, you can use validation hook :

And the code file should have the following function:

def validate_input( trans, error_map, param_values, page_param_map ):

To display an error message for particular parameter, set:

error_map['param_name'] = "Some message"

And if you print anything in validate_input(), you get the “Log messages” window in Galaxy UI:

We are at the ICCR 2013

Meet us at the International Conference on the Use of Computers in Radiation Therapy (ICCR2013) which is held in Melbourne Convention and Exhibition Centre (6-9 May 2013). The conference has many interesting themes, including: dose calculation methods, analysis/evaluation of 3D distributions, Monte Carlo modelling, verification imaging including CBCT, segmentation, data mining, image registration, imaging for planning including functional, etc. We display our poster entitled Cloud-Based Workflows Tools Supporting Medical Imaging in the Segmentation section, and here is the link. We are currently in the phase of accepting new pilot users. If you are interested please send us e-mail.

X-TRACT - CT and Imaging tools

X-TRACT - a software for advanced X-ray image analysis and Computed Tomography currently in use on the MASSIVE cluster at the Australian Synchrotron, ANU and at the Shanghai Synchrotron in China. X-TRACT implements a large number of conventional and advanced algorithms for 2D and 3D X-ray image reconstruction and simulation.

Major X-TRACT functionality is now available as part of Cloud-Based Image Analysis and Processing Toolbox. The following features are implemented:

FUNCTION	DESCRIPTION FROM END-USERS PERSPECTIVE
Sinogram creation	X-ray projection data must first be converted into sinograms before CTreconstruction can be carried out. Each sinogram contains data from a single row of detector pixels for each illuminating angles. This data is sufficient for the reconstruction of a single axial slice (at least, in parallel-beam geometry).
Ring artefact removal	Ring artefacts are caused by imperfect detector pixel elements as well as by defects or impurities in the scintillator crystals. Ring artefacts can be reduced by applying various image processing techniques on sinograms or reconstructed images.
Dark current subtraction	Dark current subtraction compensates for the readout noise, ADC offset, and dark current in the detector. The dark current images are collected before and/or after CT measurements with no radiation applied and with the same integration time as the one used during the measurements. The dark current image is subtracted from each CT projection.
Flat field correction	Flat-field images are obtained under the same conditions as the actual CT projections, but without the sample in the beam. They allow one to correct the CT projections for the unevenness of the X-ray illumination.
Positional drift correction	The function is used for correction of transverse drift between related experimental images.  Image drift is assessed by cross-correlating pairs of images.
Data normalisation	Data normalisation
TIE-based phase extraction	The TIE algorithm allows the recovery of the optical phase of an electromagnetic wave (e.g. an X-ray beam) from a single near-field in-line image by solving the Transport of Intensity equation under the assumption that the phase shift and absorption distributions are proportional to each other. This method is usually applied in propagation-based in-line CT imaging (PCI-CT).
FBP CT reconstruction	Filtered back-projection (FBP) parallel-beam CT reconstruction.
Gridrec CT reconstruction	High speed CT reconstruction algorithm.
Centre of rotation	Automated calculation of the centre of sample rotation in a CT scan from experimental X-ray projections, sinograms or reconstructed axial slices.
CT Reconstruction Filters	The choice of available CT reconstruction filters will include at least the Liner-Ramp, Shepp-Logan, Cosine, Hamming and Hann filters.
ROI reconstruction	This option enables the user to select a subset of axial slices to be reconstructed and/or limit the reconstruction area to a user-defined rectangular subarea of the axial slice. The option reduces the reconstruction time and the size of the output data.

 And here's a short video showing the basic usage of X-TRACT in Galaxy cloud:

HCA-Vision Components in Cloud-based Image Analysis and Processing Toolbox Ready to Test

HCA-Vision components in Cloud-based Image Analysis and Processing Toolbox are ready to test. Here is a video clip showing an example of how to build a workflow using some of the tools in the toolbox:

Enjoy using the toolbox and look forward to its release in the near future!