The ANU micro-CT lab is host to several 3D X-ray imaging systems (a.k.a. X-ray micro-tomography scanners) that can produce up to ~240GB of data per machine, per day. These scanners provide valuable information on geological, paleontological, and biological specimens. Further increases in the data rate and number of machines will necessitate the use of some sort of compression algorithm to store the recorded data.
The student will initially assess the practicality of various lossy and lossless compression algorithms. Any lossy compression will introduce defects into the recorded X-ray data; the student will conduct a mathematical and empirical evaluation of the effect that these defects have on: (i) the synthesis of 3D images from the recorded X-ray data; and (ii) the subsequent computational analysis of these 3D images. This investigation may lead to new algorithms to perform either or both of these steps.
Finally, research into "compressed sensing" methods shows that a successful data compression method can actually be used to improve the synthesis of 3D images from the recorded X-ray data; this will be explored if the student has time.
Willingness to engage with mathematical, physical, and computational disciplines. Familiarity with python/c/c++ is a bonus.