Tracking Monotonically Advancing Boundaries in Image Sequences Using Graph Cuts and Recursive Kernel Shape Priors
We introduce a probabilistic computer vision technique to track monotonically advancing boundaries of objects within image sequences. Our method incorporates a novel technique for including statistical prior shape information into graph-cut based segmentation, with the aid of a majorization-minimization algorithm. Extension of segmentation from single images to image sequences then follows naturally using sequential Bayesian estimation. Our methodology is applied to two unrelated sets of real biomedical imaging data, and a set of synthetic images. Our results are shown to be superior to manual segmentation.
graph cut shape prior segmentation bayesian vision contour tracking cortical spreading depression gaussian markov random fields gaussian process level set method optical intrinsic signal imaging particle filter shape statistics wound healing assay