  Image segmentation is a crucial but challenging task that has many
applications. In medical imaging for instance, intensity inhomogeneity and
noise are common. In thigh muscle images, different muscles are closed packed
together and there are often no clear boundaries between them. Intensity based
segmentation models cannot separate one muscle from another. To solve such
problems, in this work we present a segmentation model with adaptive spatial
priors from joint registration. This model combines segmentation and
registration in a unified framework to leverage their positive mutual
influence. The segmentation is based on a modified Gaussian mixture model
(GMM), which integrates intensity inhomogeneity and spacial smoothness. The
registration plays the role of providing a shape prior. We adopt a modified sum
of squared difference (SSD) fidelity term and Tikhonov regularity term for
registration, and also utilize Gaussian pyramid and parametric method for
robustness. The connection between segmentation and registration is guaranteed
by the cross entropy metric that aims to make the segmentation map (from
segmentation) and deformed atlas (from registration) as similar as possible.
This joint framework is implemented within a constraint optimization framework,
which leads to an efficient algorithm. We evaluate our proposed model on
synthetic and thigh muscle MR images. Numerical results show the improvement as
compared to segmentation and registration performed separately and other joint
models.
