Date of Award:
2012
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Computer Science
Advisor/Chair:
Dr. Nicholas Flann
Abstract
Understanding complex interactions in cellular systems requires accurate tracking of individual cells observed in microscopic image sequence and acquired from multi-day in vitro experiments. To be effective, methods must follow each cell through the whole experimental sequence to recognize significant phenotypic transitions, such as mitosis, chemotaxis, apoptosis, and cell/cell interactions, and to detect the effect of cell treatments. However, high accuracy long-range cell tracking is difficult because the collection and detection of cells in images is error-prone, and single error in a one frame can cause a tracked cell to be lost. Detection of cells is especially difficult when using bright field microscopy images wherein the contrast difference between the cells and the background is very low. This work introduces a new method that automatically identifies and then corrects tracking errors using a combination of combinatorial registration, flow constraints, and image segmentation repair.
Recommended Citation
Hayrapetyan, Nare, "Adaptive Re-Segmentation Strategies For Accurate Bright Field Cell Tracking" (2012). All Graduate Theses and Dissertations. Paper 1230.
http://digitalcommons.usu.edu/etd/1230
Copyright for this work is retained by the student.
Comments
This work made publicly available electronically on May 11, 2012.