Atlas-based segmentation of medical images

Akinyemi, Akinola Olanrewaju (2011) Atlas-based segmentation of medical images. EngD thesis, University of Glasgow.

Abstract

Atlas-Based Segmentation of medical images is an image analysis task which involves labelling a desired anatomy or set of anatomy from images generated by medical imaging modalities. The overall goal of atlas-based segmentation is to assist radiologists in the detection and diagnosis of diseases. By extracting the relevant anatomy from medical images and presenting it in an appropriate view, their work-flow can be optimised.

This portfolio-style thesis discusses the research projects carried out in order to evaluate the applicability of atlas-based methods to a variety of medical imaging problems. The thesis describes how atlas-based methods have been applied to heart segmentation, to extract the heart for further cardiac analysis from cardiac CT images, to kidney segmentation, to prepare the kidney for automated perfusion measurements, and to coronary vessel tracking, in order to improve on the quality of tracking algorithms.

This thesis demonstrates how state of the art atlas-based segmentation techniques can be applied successfully to a range of clinical problems in different imaging modalities. Each application has been tested using not only standard experimentation principles, but also by clinically-trained personnel to evaluate its efficacy. The success of these methods is such that some of the described applications have since been deployed in commercial products.

While exploring these applications, several techniques based on published literature were explored and tailored to suit each individual application. This thesis describes in detail the methods used for each application in turn, recognising the state of the art, and outlines the author's contribution in every application.

• Item Type: Thesis (EngD)
• Qualification Level: Postdoctoral
• Keywords: Medical Image Analysis, Segmentation, Machine Learning, Classification
• Subjects: R Medicine > R Medicine (General); T Technology > T Technology (General)
• Colleges/Schools: College of Science and Engineering > School of Engineering
• Supervisor's Name: Petillot, Professor Yvan and Poole, Dr. Ian
• Date of Award: 2011
• Depositing User: Mr Akinola Olanrewaju Akinyemi
• Unique ID: glathesis:2011-2623
• Copyright: Copyright of this thesis is held by the author.
• Date Deposited: 03 Jun 2011
• Last Modified: 21 Aug 2018 11:22
• URI: https://theses.gla.ac.uk/id/eprint/2623