Level-set evolution with region competition: automatic 3-d segmentation of brain tumors

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 conven-tional statistical classification and mathematical morphol-ogy, however snakes with constant propagation need careful initialization and can leak through weak or missing bound-ary 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 calcu-late 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. 1