Image processing in echography and MRI

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.

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