  Segmentation of images holds an important position in the area of image
processing. It becomes more important whi le typically dealing with medical
images where presurgery and post surgery decisions are required for the purpose
of initiating and speeding up the recovery process. Segmentation of 3-D tumor
structures from magnetic resonance images (MRI) is a very challenging problem
due to the variability of tumor geometry and intensity patterns. Level set
evolution combining global smoothness with the flexibility of topology changes
offers significant advantages over the conventional statistical classification
followed by mathematical morphology. Level set evolution with constant
propagation needs to be initialized either completely inside or outside the
tumor and can leak through weak or missing boundary parts. Replacing the
constant propagation term by a statistical force overcomes these limitations
and results in a convergence to a stable solution. Using MR images presenting
tumors, probabilities for background and tumor regions are calculated from a
pre- and post-contrast difference image and mixture modeling fit of the
histogram. The whole image is used for initialization of the level set
evolution to segment the tumor boundaries.
