Imaging and Computational Methods for Exploring Sub-cellular Anatomy

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Title: Imaging and Computational Methods for Exploring Sub-cellular Anatomy
Author: Mayerich, David
Abstract: The ability to create large -scale high -resolution models of biological tissue provides an excellent opportunity for expanding our understanding of tissue structure and function . This is particularly important for brain tissue , where the majority of function occurs at the cellular and sub -cellular level . However , reconstructing tissue at sub -cellular resolution is a complex problem that requires new methods for imaging and data analysis . In this dissertation , I describe a prototype microscopy technique that can image large volumes of tissue at sub -cellular resolution . This method , known as Knife -Edge Scanning Microscopy (KESM ) , has an extremely high data rate and can capture large tissue samples in a reasonable time frame . We can therefore image complete systems of cells , such as whole small animal organs , in a matter of days . I then describe algorithms that I have developed to cope with large and complex data sets . These include methods for improving image quality , tracing filament networks , and constructing high -resolution anatomical models . These methods are highly parallel and designed to allow users to segment and visualize structures that are unique to high -throughput microscopy data . The resulting models of large -scale tissue structure provide much more detail than those created using standard imaging and segmentation techniques .
URI: http : / /hdl .handle .net /1969 .1 /ETD -TAMU -2009 -05 -745
Date: 2010-01-16

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Imaging and Computational Methods for Exploring Sub-cellular Anatomy. Available electronically from http : / /hdl .handle .net /1969 .1 /ETD -TAMU -2009 -05 -745 .

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