Many biological and medical imaging modalities produce large arrays of spatially-correlated time series. For example, these include EEG, fMRI, and calcium imaging. A common problem in the analysis of such problems is to organise the image nodes/pixels/voxels into groups that share similarities and that are different from other groups. In machine learning and statistics, this problem is known as clustering. A common approach to clustering is via the use of mixture models, which produce model-based clusterings of the data. Model-based clustering techniques can be slow due to memory-intensive and computationally-expensive algorithms. We propose an approach based on trimmed algorithms, which can be implemented with less memory and computational strain. An example of a zebrafish brain calcium image is presented to demonstrate the approach.

Dr Hien D. Nguyen is a Postdoctoral Research Fellow at the Centre for Advanced Imaging and School of Mathematics and Physics, UQ.
 

About CAI Seminar Series

The perfect opportunity to attend cutting-edge research presentations involving CAI researchers or collaborators, each Tuesday at 9:30am in the CAI Seminar Room, entry via CAI main doors, facing Wep Harris oval (see map).

If you would like weekly email notification for the seminar series or are interested in presenting, please contact Dr Lorine Wilkinson.

Venue

Building 57
Room: 
Level 2 Seminar Room

Other upcoming sessions