Scalable Learning: Probabilistic Graphical Models for Image Analysis

Fig. 1: A 3D data volume from the microCT beamline at the ALS (left) undergoes image segmentation (right) as part of an analysis pipeline.

Scientific Achievement

Leveraging advances in mathematics and CS, a new method for graph partitioning scales image segmentation to run on large DOE HPC platforms, and with high accuracy [1].

Significance and Impact

This new method enables accelerating image analysis time-to-solution for DOE science projects challenged by an ever-growing data tsunami.

Research Results

The method exploits cliques from a Markov Random Field (MRF) formulation, and its solution yields a highly accurate N-label image segmentation
Reduced computational complexity, suitable for use on large problem sizes
Parallel parameter learning for MRF uses LAP strategy for graph partitioning
Shared- and distributed-memory implementations run on DOE HPC platforms

Contact

Talita Perciano

Bibliography

C. Heinemann, T. Perciano, D. Ushizima, and E. W. Bethel, “Distributed Memory Parallel Markov Random Fields Using Graph Partitioning,” in 2017 IEEE International Conference on Big Data (Big Data), pp. 3332–3341, Dec. 2017.
```
@inproceedings{Heinemann:BigData:2017,
  author = {Heinemann, C. and Perciano, T. and Ushizima, D. and Bethel, E. W.},
  booktitle = {2017 IEEE International Conference on Big Data (Big Data)},
  title = {{Distributed Memory Parallel Markov Random Fields Using Graph Partitioning}},
  year = {2017},
  pages = {3332--3341},
  doi = {10.1109/BigData.2017.8258318},
  escholarshipurl = {https://escholarship.org/uc/item/0g13f631},
  month = dec
}
```