Date of Award:

5-2012

Document Type:

Thesis

Degree Name:

Master of Science (MS)

Department:

Computer Science

Committee Chair(s)

Nicholas Flann

Committee

Nicholas Flann

Committee

Daniel Watson

Committee

Renée Bryce

Abstract

Cell migration is central for many fundamental biological processes and development of multi-cellular organisms. The failure of cells to migrate or migration of cells to inappropriate locations during embryo development can result in life threatening consequences such as brain malfunctions. In adults, cell migration plays an important role in wound healing and immune responses. Failure in these processes can have dramatic medical implications and can lead to vascular diseases and tumor formation. Therefore, studying cell migration is critical to helping prevent and cure diseases.


Cell migration is usually studied by observing cells photographed through a microscope at regular time intervals. However, because of the sheer amount of data, this is a time-consuming and difficult task. There is an obvious need for automated approaches that can correctly identify and follow cells. Many such approaches exist, but they are very error-prone due to poor image quality and difficulty of the task. Here, we present an approach for automatically detecting and correcting those errors, which will increase the accuracy of automated methods.

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03286fcc0fdda47e7ab22798b02288b2

Comments

This work made publicly available electronically on May 11, 2012.

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