Quantitative AnalystGoogle Inc
High-level Image Understanding Through Bayesian Hierarchical Models
The tasks performed by medical image analysis technicians, including registration and segmentation, have become increasingly difficult with the advent of three-dimensional imaging systems. To identify features in these large images, the technician must typically engage in the tedious chore of examining numerous lower dimensional representations of parts of the data set, for instance slices though the volume or volume-rendered views. The pursuit of automatic image understanding, previously sought after in two-dimensional images for objective anatomical measurement and to reduce operator burden, therefore has become proportionally more valuable in these larger image datasets. A statistical framework is proposed to automate image feature identification and therefore facilitate the image understanding tasks of registration and segmentation. Features are delineated using an atlas image, and a probability distribution is defined on the locations and variations in appearance of these features in new images from the class exemplified by the atlas. The predictive distribution defined on feature locations in a new image from the class essentially balances the two notions that, while each individual feature in the new image should appear similar to its atlas representation, contiguous groups of features should also remain faithful to their spatial relationships in the atlas image. A joint hierarchical model on feature locations facilitates reasonable spatial deformations from the atlas configuration, and several local image measures are explored to quantify feature appearance. The hierarchical structure of the joint distribution on feature locations allows fast and robust density maximization and straightforward Markov Chain Monte Carlo simulation. Model hyperparameters can be estimated using training data in the form of manual feature observations. Given Maximum posteriori estimates an analysis is performed on in vitro mouse brain Magnetic Resonance images to automatically segment the hippocampus. The model is also applied to time-gated Single Photon Emission Computed Tomography cardiac images to reduce motion artifact and increase signal-to-noise.