PhD Defense: "Spatio-Temporal Reconstruction Techniques for Optical Microscopy"

Nikhil Chacko

September 25th (Friday), 11:00am
Harold Frank Hall (HFH), Rm 4164

Optical microscopy offers the unique possibility to study living samples under conditions akin to their native state. However, the technique is not void of inherent problems such as optical blur due to light diffraction, contamination with out-of-focus light from adjacent focal planes, and spherical aberrations. Furthermore, with a dearth of techniques that are capable of imaging multiple focal sections in quick succession, the multi-dimensional capture of dynamically changing samples remains a challenge of its own. Computational techniques that use auxiliary knowledge about the imaging system and the sample to mitigate these problems are hence of great interest in optical microscopy. The first part of this thesis deals with the design of a discrete model to characterize light propagation. Following the scalar diffraction theory in optics, we propose a multi-rate discrete algorithm, based on generalized sampling theory, to reverse the coherent diffraction process via back propagation. The second part of this thesis describes a spatial registration tool designed for multi-view microscopy, where the imaged sample is rotated about a lateral axis for the acquisition of multiple 3D datasets from different views in order to subsequently alleviate the severe axial blur found in each such dataset. Automatic algorithms that only rely on maximizing pixel-based similarity provide poor results in such applications owing to the anisotropic point-spread-function (PSF) of optical microscopes. We propose a pyramid-based spatial registration algorithm that re-blurs the multi-view datasets with geometrically transformed forms of the PSF in order to make them comparable, before maximizing their pixel based similarity for registration. The third part of this thesis describes a fast converging iterative multi-view deconvolution technique that can be applied to the spatially registered 3D datasets acquired using multi-view microscopy. The fourth part of this thesis addresses problems due to spherical aberrations encountered during the imaging of thick samples in optical microscopy. We propose a fast iterative shrinkage-thresholding depth-variant 3D deconvolution method that uses depth-dependent PSFs. The final part of this thesis describes a non-rigid temporal registration tool that aids in the multi-dimensional imaging of quasi-periodic processes such as cardiac cycles. We propose a variant of dynamic time warping that is capable of both temporally warping and wrapping an input sequence by allowing for jump discontinuities in the non-linear temporal alignment function akin to those found in wrapped phase functions.

About Nikhil Chacko:

photo of nikhil chacko Nikhil Chacko received his B.E. degree in Electronics and Communication Engineering from the National Institute of Technology, Calicut, India, in 2010. He secured his M.S. degree in Electrical and Computer Engineering (ECE) from the University of California, Santa Barbara (UCSB), in 2012, where he is currently pursuing his Ph.D. degree under the guidance of Prof. Michael Liebling. His research interests include signal processing, inverse image reconstruction, spatio-temporal image registration, and computer vision. He was a recipient of the 'Best Student Paper Award' at IEEE International Symposium on Biomedical Imaging in 2015, the 'ECE Outstanding Teaching Assistant Award' at UCSB in 2015, and the 'ECE Spring Dissertation Fellowship' at UCSB in 2014. He is expected to join Amazon Corporate LLC, Seattle, as a research scientist, following graduation.

Hosted by: Professor Michael Liebling