On 8-12 October 2012, we travelled to Chicago to attend the 8th IEEE International Conference on eScience (eScience 2012) and the Microsoft eScience Workshop. At the conference we presented our poster entitled Cloud-Based Image Analysis and Processing Toolbox for Biomedical Application.
15 October 2012
10 July 2012
The Cloud-based Image Analysis and Processing Toolbox project is to provide improved access to the existing biomedical image processing and analysis software packages to research communities via remotely accessible user‐interfaces, the execution of which is carried out on the NeCTAR supported cloud infrastructure.
These software packages have been developed over the last 10‐15 years by CSIRO scientists and software engineers, and they include:
- HCA‐Vision: developed for automating the process of quantifying cell features in microscopy images. It can reproducibly analyse complex cell morphologies. Recently extended to 3‐D, it enables the analysis of neuron structures in vitro (in cells cultured in a 3‐D gel matrix) and in vivo (e.g. in exposed rat brains or viable rat brain tissue sections).
- MILXView: a 3D medical imaging analysis and visualisation platform increasingly popular with researchers and medical specialists working with MRI, PET and other types of medical images. A suite of functions have been developed for viewing and processing 3D and 4D medical data. Several advanced processing pipelines also exist, such as a fully automated brain morphometry estimate from MRI.
- X‐TRACT: developed 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.
The toolbox will unify those software packages in the form of libraries for image analysis, processing and 3D reconstruction algorithms to run in the cloud environment for high computation tasks and/or large image datasets. This will dramatically increase the productivity of designing processing pipeline and accelerate scientific discoveries. By providing user‐friendly access to cloud computing resources and new workflow‐based interfaces, our solution will enable the researchers to carry out many challenging image analysis and reconstruction tasks that are currently impossible or impractical due to the limitations of the existing interfaces and the local computer hardware.