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SEGMENTATION OF FETAL 3D ULTRASOUND BASED ON STATISTICAL PRIOR AND DEFORMABLE MODEL
 

Summary: SEGMENTATION OF FETAL 3D ULTRASOUND BASED ON STATISTICAL PRIOR AND
DEFORMABLE MODEL
Jérémie Anquez, Elsa D. Angelini, Isabelle Bloch
Institut TELECOM, TELECOM ParisTech, LTCI CNRS, Paris, France.
ABSTRACT
A statistical variational framework is proposed for the fetus and
uterus segmentation in ultrasound images. The Rayleigh and ex-
ponential distributions are used to model the pixel intensity. An
energy is derived to perform an optimal partition of the 3D data into
two classes corresponding to these two distributions, in a Bayesian
MAP framework. Some numerical difficulties are raised by the
combination of heterogeneous distributions in a variational level-set
formulation, as discussed in the paper. Results on simulated and real
data are presented and show that assuming different distributions
provides better results than with the sole Rayleigh distribution.
Index Terms-- 3D ultrasound, segmentation, deformable
model, statistical prior
1. INTRODUCTION
Ultrasounds imaging (echography) is the main imaging modality
used for pregnancy follow up [1], combining several advantages such

  

Source: Angelini, Elsa -Département Traitement du Signal et des Images, Telecom ParisTech

 

Collections: Engineering; Biology and Medicine