Medical image analysis is a ubiquitous and essential part of modem health care. A
crucial first step to this is segmentation, which is often complicated by many factors
including subject diversity, pathology, noise corruption, and poor image resolution.
Traditionally, manual tracing by experts was done. While considered accurate, this
process is time consuming and tedious, especially when performed slice-by-slice on
three-dimensional (3D) images over large datasets or on two-dimensional (2D) but
topologically complicated images such as a retinography. On the other hand, fully-automated
methods are typically faster, but work best with data-dependent, carefully
tuned parameters and still require user validation and refinement.
This thesis contributes to the field of medical image segmentation by proposing a
highly-automated, interactive approach that effectively merges user knowledge and
efficient computing. To this end, our work focuses on graph-based methods and offer
globally optimal solutions. First, we present a novel method for 3D segmentation based
on a 3D Livewire approach. This approach is an extension of the 2D Livewire
framework, and this method is capable of handling objects with large protrusions,
concavities, branching, and complex arbitrary topologies. Second, we propose a method
for efficiently segmenting 2D vascular networks, called ‘Live-Vessel’. Live-Vessel
simultaneously extracts vessel centrelines and boundary points, and globally optimizes
over both spatial variables and vessel radius. Both of our proposed methods are validated
on synthetic data, real medical data, and are shown to be highly reproducible, accurate,
and efficient. Also, they were shown to be resilient to high amounts of noise and
insensitive to internal parameterization.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:BVAU./2736 |
| Date | 05 1900 |
| Creators | Poon, Miranda |
| Publisher | University of British Columbia |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Electronic Thesis or Dissertation |
| Format | 2313900 bytes, application/pdf |
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