Scalable Learning: Probabilistic Graphical Models for Image Analysis

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

• Talita Perciano

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.
   BibTeX

   @article{Perciano:pp5100,
     author = {Perciano, Talita and Ushizima, Daniela and Krishnan, Harinarayan and Parkinson, Dilworth and Larson, Natalie and Pelt, Dani{\"{e}}l M. and Bethel, Wes and Zok, Frank and Sethian, James},
     doi = {10.1107/S1600577517010955},
     journal = {Journal of Synchrotron Radiation},
     keywords = {unsupervised segmentation, micro-tomography, ceramic matrix composites, image analysis},
     month = sep,
     number = {5},
     pages = {1065--1077},
     title = {{Insight into 3D micro-CT data: exploring segmentation algorithms through performance metrics}},
     url = {https://doi.org/10.1107/S1600577517010955},
     volume = {24},
     year = {2017},
     escholarshipurl = {https://escholarship.org/uc/item/5q32w3rb}
   }