Image analysis of swimming microorganisms

• Main Author: Loh, Shaun Pei Hao
• Other Authors: School of Mechanical and Aerospace Engineering
• Format: Final Year Project
• Language: English
• Published: 2014
• Subjects: DRNTU::Engineering::Mechanical engineering
• Online Access: http://hdl.handle.net/10356/61309
• Institution: Nanyang Technological University

Microorganisms are ubiquitous in our lives and they are the basis of all kingdoms of life, living in almost every part of the earth. The large diversity of microorganisms separates them into many different classifications such as bacteria and fungi. Every class of microorganism serves a different function, thus playing a vital role in our lives and environment. Microorganisms have both beneficial and detrimental roles on earth such as recycling nutrients in ecosystems through decomposition, but they also cause diseases which kill large populations of living creatures every year. Locomotion of microorganisms has long been an area of interest for biologists and physicists as it forms the backbone behind various cell functions and processes such as protein transport. Due to the vast nature of microorganisms, it is important to note that different classifications of microorganisms have different methods of locomotion and each separate mode has their own characteristic features that would result in different locomotive abilities. This project serves to conduct a comprehensive study on the possibilities of locomotion in microorganisms characterized by a flagellum, namely bacteria and spermatozoa. By providing a greater understanding of their locomotion, further possible predictions and analysis can be made such as the directional spread of bacteria in a host organism. The project consists of two major segments, mainly, flagellum profile recognition and particle tracking. Flagellum profile recognition seeks to extract detailed information about the flagellum profile that would characterize its locomotive abilities. Particle tracking would then serve as a supplement to the profile recognition by assessing the actual locomotion of the microorganisms. Both segments would be done using a software algorithm to extract and process the information.