Electron tomography allows determination of three-dimensional
structures of cells and tissues at resolutions significantly higher
than is possible with optical microscopy. The development of reliable quantitative approaches for interpretation of features in tomograms, is an important problem, but is a challenging prospect because of the low signal-to-noise ratios that are inherent to biological electron
microscopic images.
In this talk we describe novel 3D geometric flows used to robustly segment electron microscopy data, and we show preliminary applications in HIV research.
This work is a collaboration between the speaker, Prof. Sapiro, and
the NIH lab of Prof. Subramaniam.
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