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.
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Keywords
- Probability Density Function
- Prediction Error
- Resolution Cell
- Prediction Error Variance
- Royal North Shore Hospital
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© 1997 Springer-Verlag Berlin Heidelberg
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Abbott, P., Braun, M. (1997). Segmentation of ultrasound image data by two dimensional autoregressive modelling. In: Del Bimbo, A. (eds) Image Analysis and Processing. ICIAP 1997. Lecture Notes in Computer Science, vol 1311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-63508-4_182
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DOI: https://doi.org/10.1007/3-540-63508-4_182
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