Abstract:
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.
The pdf of the paper.
The slides for my talk.