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)


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.


Probability Density Function Prediction Error Resolution Cell Prediction Error Variance Royal North Shore Hospital 
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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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