We develop a new method for automatic segmentation of anatomical structures from volumetric medical images. Driving application is tumor segmentation from 3-D MRIs, which is known to be a very challenging problem due to the variability of tumor geometry and intensity patterns. Level-set snakes offer significant advantages over conventional statistical classification and mathematical morphology, however snakes with constant propagation need careful initialization and can leak through weak or missing boundary parts. Our region competition method overcomes these problems by modulating the propagation term with a signed local statistical force, leading to a stable solution.

A pre- vs. post-contrast difference image is used to calculate probabilities for background and tumor regions, with a mixture-modelling fit of the histogram. Preliminary results on five cases with significant shape and intensity variability demonstrate that the new method might become a powerful and efficient tool for the clinic. Validity is demonstrated by comparison with manual expert segmentation.