We develop a 3D region tracking method based on Maximum A Posteriori (MAP) tracker and adapt it to digital hologram sequences to efficiently track biological microorganisms in holographic microscopy data. In our approach, the target surface is modeled as the iso-surface of a level set function which is evolved at each frame via level set Hamilton Jacobian update rule in Euler-Lagrangian framework. The statistical characteristics of the target microorganism versus the background are exploited to evolve the interface at each frame, thus the algorithm works independent of the shape or morphology of the target. We use the bivariate Gaussian distribution to model the reconstructed hologram data which enables us to take into account the correlation between the amplitude and phase of the reconstructed wavefront to obtain a more accurate tracking solution.
© 2007 Optical Society of America
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