Automated analysis of deformable structure in groups of images

We describe an approach for automated analysis of deformable objects which extracts structure information from groups of images containing different examples of the object with a particular application to human imaging. The proposed analysis framework simultaneously segments and registers a set of images, incrementally constructing a model of the composition of the object. By fitting an appropriate intensity distribution model to the image we obtain a soft segmentation which allows us to explicitly model the construction of each pixel from constituent image segments, rather than its expected intensity. This effectively decouples the model from the effects of the imaging system and varying statistics in different examples. When estimating the optimal deformation field for each example, the original image is compared to a reconstruction, generated using the composition model and its intensity distribution parameters for each segment (i.e. an estimate of how the model would appear given the imaging conditions for that image). In the paper we describe the algorithm in detail and show results of applying it to two sets of medical images of different anatomies taken with different imaging modalities. We present quantitative results demonstrating that the proposed algorithm is more powerful than current state of the art methods at extracting structural information such as spatial correspondences across groups of images with varying statistics.