Cloud-Based Image Analysis and Processing Toolbox
3 December 2013
User Manual available online
Finally, the very first version of user manual is available online now under the following link.
13 November 2013
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:
30 September 2013
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.
28 May 2013
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 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:
7 May 2013
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.
29 April 2013
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.
|
28 April 2013
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!
Enjoy using the toolbox and look forward to its release in the near future!
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