Tumor segmentation from MRI data is a particularly challenging and time consuming task. Tumors have a large diversity in
shape and appearance with intensities overlapping the normal brain tissues. In addition, an expanding tumor can also deflect
and deform nearby tissue. Our work addresses these last two difficult problems. We use the available MRI modalities (T1,
T1c, T2) and their texture characteristics to construct a multi-dimensional feature set. Further, we extract clusters which
provide a compact representation of the essential information in these features. The main idea in this paper is to incorporate
these clustered features into the 3D variational segmentation framework. In contrast to the previous variational approaches,
we propose a segmentation method that evolves the contour in a supervised fashion. The segmentation boundary is driven
by the learned inside and outside region voxel probabilities in the cluster space. We incorporate prior knowledge about
the normal brain tissue appearance, during the estimation of these region statistics. In particular, we use a Dirichlet prior
that discourages the clusters in the ventricles to be in the tumor and hence better disambiguate the tumor from brain tissue.
We show the performance of our method on real MRI scans. The experimental dataset includes MRI scans, from patients
with difficult instances, with tumors that are inhomogeneous in appearance, small in size and in proximity to the major
structures in the brain. Our method shows good results on these test cases.