Segmentation of ultrasound image data by two dimensional autoregressive modelling

  • Phillip Abbott
  • Michael Braun
Session 11: Biomedical Applications
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1311)

Abstract

In this paper we treat ultrasound image data as a two dimensional autoregressive (AR) signal. The image is modelled as consisting of distinct regions each described by one of a small number of AR models. Segmentation is performed by maximising the image likelihood function, which takes on a convenient form due to the AR model. Image data is presented to the algorithm in complex amplitude form. Results from application of this method to a cardiac phantom data set are presented.

Keywords

Probability Density Function Prediction Error Resolution Cell Prediction Error Variance Royal North Shore Hospital 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1997

Authors and Affiliations

  • Phillip Abbott
    • 1
    • 2
  • Michael Braun
    • 1
    • 2
  1. 1.Co-operative Research Centre for Cardiac TechnologyRoyal North Shore HospitalSt. LeonardsAustralia
  2. 2.Department of Applied PhysicsUniversity of Technology, SydneyBroadwayAustralia

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