CLUSTERING ON LOCAL APPEARANCE FOR DEFORMABLE MODEL SEGMENTATION

We present a novel local region approach for statistically characterizing appearance in the context of medical image segmentation via deformable models. Our appearance model reflects the inhomogeneity of tissue mixtures around the exterior of the object of interest by determining mixture-consistent local region types relative to the object boundary. The region types are formed by clustering local regional image descriptors. We partition the object boundary according to region type and apply principal component analysis on the cluster populations to acquire a statistical model of object appearance that accounts for local variability in the object exterior. We present results using this approach to segment bladders and prostates in CT in the context of day-to-day adaptive radiotherapy for prostate cancer. Results show improved fits versus those obtained with a previously developed method