professor geraint f. lewis sydney institute for astronomy school of physics the university of sydney
Post on 19-Dec-2015
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TURNING THE TELESCOPE INWARDS:SYNERGIES BETWEEN MEDICAL IMAGING & ASTRONOMY
Professor Geraint F. LewisSydney Institute for AstronomySchool of PhysicsThe University of Sydney
Modern Astronomy
Observing techniques Direct imaging Spectral imaging Interferometry Polarization Monitoring Neutrinos &
gravitational waves
Survey Imaging: A history
In the 1930s, the Schmidt corrector plate allowed astronomers to photograph large patches of sky. Typically, these were made in several colours, allowing the creation of true-colour images (which are loved by the public ).
Survey Imaging: A history
The photographic plates were digitized with a laser scanner (i.e. the Automatic Plate Measuring - APM) machines. However, a single scan was ~4GB in size, a problem for 1980s technology, data storage and handling.
Survey Imaging: A history
Images were reduced to “catalogues” of objects;
Characterization of the noise (limiting brightness)
Identifying objects (contiguous pixels) Measurement of object properties (e.g.
shape) Object classification Astrometric registration (to sky coordinates) Absolute brightness calibration
The left-hand panel is red light, whereas the right-hand panel is blue. Notice that there are colour-differences between objects, allowing identification.
Survey Imaging: A history
The PathGrid Project
VO-AstroGrid techniques applied to Tissue Micro-Array (TMA) meta-data. Automated pattern recognition to identify and assess potential genetic biomarkers to improve clinical outcomes for cancer treatment.
Conclusions
Astronomy has, and will be, technology and data driven.
Groups are actively developing methods to maximize return from data collection.
In the future, data, techniques and tools will be accessible to the community, with the goal of bleeding science from data.
Such approaches are being adopted by medical science and links are beneficial.
Applying Astrogrid Techniques to the Analysis of Tissue Microarrays
Antibody hybridization and image scanning of tissue microarrays (TMAs) is a highly automated process, but the subsequent manual scoring of TMAs by a trained pathologist is a major bottleneck in their analysis. We can overcome this bottleneck by applying techniques developed for astronomical imaging data, as part of the global Virtual Observatory initiative, to the analysis of TMA images, and build pipelines which automate the analysis, acquisition and querying of TMA data.The Pathgrid system should have a significant impact in improving the quantitative analysis of a range of TMA markers, providing increased throughput and objective assessment of expression.