College of Computing, Engineering & Construction
Master of Science in Computer and Information Sciences (MS)
NACO controlled Corporate Body
University of North Florida. School of Computing
Dr. Sherif Elfayoumy
Dr. Roger Eggen
Dr. Sanjay Ahuja
Dr. Sherif Elfayoumy
Dr. William F. Klostermeyer
The research presented here supports the ongoing need for automatic heart volume calculation through the identification of the left and right ventricles in MRI images. The need for automated heart volume calculation stems from the amount of time it takes to manually processes MRI images and required esoteric skill set. There are several methods for region detection such as Deep Neural Networks, Support Vector Machines and Ant Colony Optimization. In this research Ant Colony Optimization (ACO) will be the method of choice due to its efficiency and flexibility. There are many types of ACO algorithms using a variety of heuristics that provide advantages in different environments and knowledge domains. All ACO algorithms share a foundational attribute, a heuristic that acts in conjunction with pheromones. These heuristics can work in various ways, such as dictating dispersion or the interpretation of pheromones. In this research a novel heuristic to disperse and act on pheromone is presented. Further, ants are applied to more general problem than the normal objective of finding edges, highly qualified region detection. The reliable application of heuristic oriented algorithms is difficult in a diverse environment. Although the problem space here is limited to MRI images of the heart, there are significant difference among them: the topology of the heart is different by patient, the angle of the scans changes and the location of the heart is not known. A thorough experiment is conducted to support algorithm efficacy using randomized sampling with human subjects. It will be shown during the analysis the algorithm has both prediction power and robustness.
Birchell, Shannon Lloyd, "Trapping ACO applied to MRI of the Heart" (2019). UNF Graduate Theses and Dissertations. 862.