The University of Southampton
University of Southampton Institutional Repository

Image processing in echography and MRI

Image processing in echography and MRI
Image processing in echography and MRI
This work deals with image processing for three medical imaging applications: speckle
detection in 3D ultrasound, left ventricle detection in cardiac magnetic resonance imaging
(MRI) and flow feature visualisation in velocity MRI.

For speckle detection, a learning from data approach was taken using pattern recognition
principles and low-level image features, including signal-to-noise ratio, co-occurrence
matrix, asymmetric second moment, homodyned k-distribution and a proposed specklet
detector. For left ventricle detection, template matching was used. Forvortex detection,
a data processing framework is presented that consists of three main steps: restoration,
abstraction and tracking. This thesis addresses the first two problems, implementing
restoration with a total variation first order Lagrangian method, and abstraction with
clustering and local linear expansion.
Carmo, Bernardo S.
b9eeb242-f24d-42d8-bbbb-87a4f2ba1418
Carmo, Bernardo S.
b9eeb242-f24d-42d8-bbbb-87a4f2ba1418
Prugel-Bennett, Adam
b107a151-1751-4d8b-b8db-2c395ac4e14e

Carmo, Bernardo S. (2005) Image processing in echography and MRI. University of Southampton, School of Electronics and Computer Science, Doctoral Thesis, 138pp.

Record type: Thesis (Doctoral)

Abstract

This work deals with image processing for three medical imaging applications: speckle
detection in 3D ultrasound, left ventricle detection in cardiac magnetic resonance imaging
(MRI) and flow feature visualisation in velocity MRI.

For speckle detection, a learning from data approach was taken using pattern recognition
principles and low-level image features, including signal-to-noise ratio, co-occurrence
matrix, asymmetric second moment, homodyned k-distribution and a proposed specklet
detector. For left ventricle detection, template matching was used. Forvortex detection,
a data processing framework is presented that consists of three main steps: restoration,
abstraction and tracking. This thesis addresses the first two problems, implementing
restoration with a total variation first order Lagrangian method, and abstraction with
clustering and local linear expansion.

Text
00302041.pdf - Other
Download (7MB)

More information

Published date: April 2005
Organisations: University of Southampton

Identifiers

Local EPrints ID: 194557
URI: http://eprints.soton.ac.uk/id/eprint/194557
PURE UUID: 9004bf80-0047-467d-92c7-6c6ed4ea6e5e

Catalogue record

Date deposited: 29 Jul 2011 15:51
Last modified: 29 Jan 2020 14:40

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×