Abstract: Local Gaussian distribution fitting (LGDF) or local Rayleigh distribution fitting (LRDF) models often give
relatively poor performance on segmenting ultrasound images, due to the large bias in describing ultrasound images
by either Gaussian or Rayleigh distribution, and the lack of guidance for ultrasound images edge information during
image segmentation. To deal with these problems, an edge entropy weighted local Fisher-Tippett (FT) distribution
fitting model was presented in this paper. According to the fact that the object and background in local regions of
ultrasound images meet with different FT distributions, the proposed model adopted maximum a posteriori (MAP)
probability to derive an energy function to be minimized. The energy function was solved by the level set method.
Meanwhile, the edge entropy was included into the length regularization term as a weight function to guide the active
contour to better capture the obscure and weak edges of the object. Extensive experiments on synthetic and real ultrasound images have demonstrated that the proposed model can not only achieve an enhancement for the local FT
distribution fitting and the inclusion of the edge entropy, but also qualitatively and quantitatively outperform many of
the existing methods.

Key words: ultrasound image, edge entropy, Fisher-Tippett distribution, active contour, level set method