Use this URL to cite or link to this record in EThOS:
Title: Algorithms for automated image analysis of Schizosaccharomyces pombe cells viewed under phase-contrast/brightfield microscopy
Author: O'Brien, Jennifer Anne
Awarding Body: University of Kent
Current Institution: University of Kent
Date of Award: 2018
Availability of Full Text:
Access from EThOS:
Access from Institution:
Schizosaccharomyces pombe (S. pombe) are used as model organisms to discover intracellular interactions and if mutations have the potential to affect higher eukaryotes, such as humans. Due to their regimented growth pattern, transmitted light microscopy can be used to identify abnormalities in cells, often by simple measures such as cell length and width. Algorithms for automated image analysis of microscopy images assist biologists by providing consistent, comparable results while reducing the demand on researchers' time. This thesis introduces a novel algorithm for making ground truth datasets, a novel algorithm for segmenting S. pombe cells from phase contrast or transmitted light microscopy images, and an algorithm to measure the lengths and widths of the segmented cells. The novel manual segmentation algorithm allows for accurate and precise, form-fitting ground truth segmentations unlike currently available programs. The novel segmentation algorithm functions better than PombeX (the current existing program designed for the same problem) on both phase contrast and transmitted light microscopy images of S. pombe. The measurement-making algorithm produces length and width measurements that closely match manual measurements.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available