Scalable Learning: Probabilistic Graphical Models for Image Analysis

Fig. 1: Segmentation evaluation of Ceramic Matrix Composite microCT data

Scientific Achievement

  • Advancement of automated data analysis coming from experimental data facilities
  • MSMcam framework analyzes data and generates easily interpretable segmentation results using appropriate set of evaluation metrics for each dataset [1].

Significance and Impact

  • Unique approach to formulating metrics to determine the quality of segmentation coming out from various techniques
  • Framework tested on several scientific datasets

Research Results

  • Use a suite of segmentation algorithms simultaneously on the same data and observe results agreement using various quality metrics
  • Automatic approach towards overcoming the fact that imaging experiments vary significantly from one experiment to another
  • Supports the analysis and comparison of image segmentation results through suitable metrics even when a ground-truth is not available

Contact

Bibliography

  1. T. Perciano, D. Ushizima, H. Krishnan, D. Parkinson, N. Larson, D. M. Pelt, W. Bethel, F. Zok, and J. Sethian, “Insight into 3D micro-CT data: exploring segmentation algorithms through performance metrics,” Journal of Synchrotron Radiation, vol. 24, no. 5, pp. 1065–1077, Sep. 2017.