The segmentation of sparse MR images
This thesis develops a methodology for the segmentation of anatomical structures within "sparse" MR images. Sparse images were acquired in large numbers prior to the emergence of high-resolution MRI and they form the basis of many long term imaging studies. The term sparse refers to the fact that the volumetric image has very poor spatial resolution in the direction perpendicular to the slice plane. This leads to a significant degradation in image quality and effectively destroys the spatial continuity of the imaged object. Consequently, generic segmentation schemes --- particularly those based on voxel classification --- will yield poor results unless they have been augmented in some manner. Our Segmentation approach is based on a deformable simplex mesh surface, which iteratively interpolates extracted boundary point data. Prior information is mobilised at two levels. Boundary points are found using a matching algorithm based on a database of pre-specified piecewise constant models. These models represent possible idealised intensity profiles for the object boundary. In addition to the boundary model, there is a shape template. The template is generated from a training set of pre-segmented structures, which means that only shapes similar to those in the training set will be recovered. The segmentation proceeds in two phases. The first recovers the normal shape component, determined by the training set, whilst the second deforms smoothly from this constrained solution to produce a more veridical boundary representation. The segmentation scheme is applied to a number of sparse brain images. Qualitative validation --- accomplished by registering the surface extracted from the sparse data to a high resolution scan acquired at the same time-point --- indicates that a good approximation to the underlying boundary is obtainable from such images.