Clustering on image boundary regions for deformable model segmentation

We present a novel approach, clustering on local image profiles, for statistically characterizing image intensity in object boundary regions. In deformable model segmentation, a driving consideration is the geometry to image match, the degree to which the target image conforms to some template within the object boundary regions. The template should account for variation over a training set and yet be specific enough to drive an optimization to a desirable result. Using clustering, a template can be built that is optimal over the training data in the metric used, such as normalized correlation. We present a method that first determines local cross-boundary image profile types in the space of training data and then builds a template of optimal types. Also presented are the results of a study using this approach on the human kidney in the context of medial representation deformable model segmentation. The results show an improvement in the automatic segmentations using the cluster template, over a previously built template.