Abstract
A probabilistic method is proposed for segmentation of the knee joint. A likelihood function is formulated
that explicitly models overlapping object appearance. Priors on global appearance and geometry (including shape)
are learned from example images. Markov chain Monte Carlo methods are used to obtain samples from a posterior
distribution over model parameters from which expectations can be estimated. The result is a probabilistic segmentation
that quantifies uncertainty so that measurements such as joint space can be made with associated uncertainty.
Joint space area and mean point-to-contour distance are used for evaluation.
that explicitly models overlapping object appearance. Priors on global appearance and geometry (including shape)
are learned from example images. Markov chain Monte Carlo methods are used to obtain samples from a posterior
distribution over model parameters from which expectations can be estimated. The result is a probabilistic segmentation
that quantifies uncertainty so that measurements such as joint space can be made with associated uncertainty.
Joint space area and mean point-to-contour distance are used for evaluation.
| Original language | English |
|---|---|
| Title of host publication | Medical Image Understanding and Analysis |
| Publisher | British Machine Vision Association and Society for Pattern Recognition |
| Pages | 110-114 |
| Number of pages | 5 |
| Publication status | Published - 2006 |