Date of Award

Winter 2013

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computational Analysis and Modeling

First Advisor

Mihaela Paun

Abstract

The goal of the research developed in this dissertation is to develop a more accurate segmentation method for Affymetrix microarray images. The Affymetrix microarray biotechnologies have become increasingly important in the biomedical research field. Affymetrix microarray images are widely used in disease diagnostics and disease control. They are capable of monitoring the expression levels of thousands of genes simultaneously. Hence, scientists can get a deep understanding on genomic regulation, interaction and expression by using such tools.

We also introduce a novel Affymetrix microarray image simulation model and how the Affymetrix microarray image is simulated by using this model. This simulation model embraces all realistic biological characteristics and experimental preparation characteristics, which could have different impacts on the quality of microarray image during the real microarray experiment. The most important aspect is that this model could provide the "ground true information," which allows us to have a deep understanding on different segmentation algorithms performance.

After the simulation, the new proposed segmentation algorithm Segmentation Based Contours (SBC) method is presented as well as the modifications of the Active Contours Without the Edges (ACWE) method. By modifying the ACWE method with higher order finite difference scheme and fast scheme, we establish the new segmentation algorithm Segmentation Based Contours method. In the end, we compare the gene signal values obtained from the new proposed algorithm Segmentation Based Contours method and the best currently known method. This gene expression signal comparison is more meaningful in gene expression analysis, since it represents the whole gene expression level rather than the small transcripts hybridization abundance level. Different types of experimental comparison results will be presented to show that the new proposed Segmentation Based Contours method is more efficient and accurate.

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