Image and video analysis techniques for cellular microscopy

[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Advances in automated digital microscopy imaging made it possible to produce multi-dimensional image data that can capture dynamic characteristics of sub-cellular and cellular structures. Biologists routinely produce large volumes of bioimage time lapse data that necessitates automated algorithms for unbiased and repeatable quantitative analysis. These algorithms are the stepping stones in bioimage informatics to turn the image data into biological knowledge. Unique challenges posed by different imaging modalities and cell dynamics require a combination of accurate detection, segmentation, classification and tracking approaches tailored to address and exploit particular image characteristics. In this dissertation, we present algorithms for the analysis of microscopy image sequences to address these challenges. We propose a level set active contour approach to address accurate segmentation in phase-contrast as well as brightfield microscopy imaging that utilizes edge profiles. Our approach significantly outperforms traditional level set approaches. We show the applications of our approach to cell spreading analysis and red blood cell analysis with robust solutions for cell detection to delineate clustered cells. We also present two studies for automated classification of cells in fluorescence microscopy emphasizing the importance of choosing image features for the specific problem. Lastly, we present a fully automated cell detection and tracking approach tailored for muscle satellite cells that enables efficient and unbiased analysis of factors that promote cell motility.

Name thesis:degree_name

Ph. D.

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Computer science (MU)

Grantor dc:publisher

University of Missouri--Columbia

Year dc:date.issued

2014

Author dc:creator

• Ersoy, Ilker

Advisor dc:contributor.advisor

• Palaniappan, K. (Kannappan)

Statement dc:rights

• Access to files is limited to the campuses of the University of Missouri with SSO login.

Language dc:language.iso

eng, English

DOI

https://doi.org/10.32469/10355/46443

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/46443