Algorithmic Information Theory Applications in Bright Field Microscopy and Epithelial Pattern Formation
Algorithmic Information Theory (AIT), also known as Kolmogorov complexity, is a quantitative approach to defining information. AIT is mainly used to measure the amount of information present in the observations of a given phenomenon. In this dissertation we explore the applications of AIT in two cas...
| Main Author: | |
|---|---|
| Format: | Others |
| Published: |
DigitalCommons@USU
2015
|
| Subjects: | |
| Online Access: | https://digitalcommons.usu.edu/etd/4539 https://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=5569&context=etd |
| Summary: | Algorithmic Information Theory (AIT), also known as Kolmogorov complexity, is a quantitative approach to defining information. AIT is mainly used to measure the amount of information present in the observations of a given phenomenon. In this dissertation we explore the applications of AIT in two case studies. The first examines bright field cell image segmentation and the second examines the information complexity of multicellular patterns. In the first study we demonstrate that our proposed AIT-based algorithm provides an accurate and robust bright field cell segmentation. Cell segmentation is the process of detecting cells in microscopy images, which is usually a challenging task for bright field microscopy due to the low contrast of the images. In the second study, which is the primary contribution of this dissertation, we employ an AIT-based algorithm to quantify the complexity of information content that arises during the development of multicellular organisms. We simulate multicellular organism development by coupling the Gene Regulatory Networks (GRN) within an epithelial field. Our results show that the configuration of GRNs influences the information complexity in the resultant multicellular patterns. |
|---|