Local affine image matching and synthesis based on structural patterns

A general purpose block-to-block affine transformation estimator is described. The estimator is based on Fourier slice analysis and Fourier spectral alignment. It shows encouraging performance in terms of both speed and accuracy compared to existing methods. The key elements of its success are attributed to the ability to: 1) locate an arbitrary number of affine invariant points in the spectrum that latch onto significant structural features; 2) match the estimated invariant points with the target spectrum by the slicewise phase-correlation; and 3) use affine invariant points to directly compute all linear parameters of the full affine transform by spectral alignment. Experimental results using a wide range of textures are presented. Potential applications include affine invariant image segmentation, registration, affine symmetric image coding, and motion analysis

Development of a statistical shape and appearance model of the skull from a South African population

Statistical shape models (SSMs) and statistical appearance models (SAMs) have been applied in medical analysis such as in surgical planning, finite element analysis, model-based segmentation, and in the fields of anthropometry and forensics. Similar applications can make use of SSMs and SAMs of the skull. A combination of the SSM and SAM of the skull can also be used in model-based segmentation. This document presents the development of a SSM and a SAM of the human skull from a South African population, using the Scalismo software package. The SSM development pipeline was composed of three steps: 1) Image data segmentation and processing; 2) Development of a free-form deformation (FFD) model for establishing correspondence across the training dataset; and 3) Development and validation of a SSM from the corresponding dataset. The SSM was validated using the leave one-out cross-validation method. The first eight principal components of the SSM represented 92.13% of the variation in the model. The generality of the model in terms of the Hausdorff distance between a new shape generated by the SSM and instances of the SSM had a steady state value of 1.48mm. The specificity of the model (in terms of Hausdorff distance) had a steady state value of 2.04mm. The SAM development pipeline involved four steps: 1) Volumetric mesh generation of the reference mesh to be used in establishing volumetric correspondence; 2) Sampling of intensity values from original computed tomography (CT) images using the in-correspondence volumetric meshes; and 3) Development of a SAM from the in-correspondence intensity values. A complete validation of the SAM was not possible due to limitations of the Scalismo software. As a result, only the shapes of the incomplete skulls were reconstructed and thereby validated. The amount of missing detail, as represented by absent landmarks, affected the registration results. Complete validation of the SAM is recommended as future work, via the use of a combined shape and intensity model (SSIM)