This paper describes a method for automatically analysing and segmenting the corpus callosum from magnetic resonance images of the brain based on the widely used Active Appearance Models (AAMs) by Cootes et al. Extensions of the original method, which are designed to improve this specific case are proposed, but all remain applicable to other domain problems. The well-known multi-resolution AAM optimisation is extended to include sequential relaxations on texture resolution, model coverage and model parameter constraints. Fully unsupervised analysis is obtained by exploiting model parameter convergence limits and a maximum likelihood estimate of shape and pose. Further, the important problem of modelling object neighbourhood is addressed. Finally, we describe how correspondence across images is achieved by selecting the minimum description length (MDL) landmarks from a set of training boundaries using the recently proposed method of Davies et al. This MDL-approach ensures a unique parameterisation of corpus callosum contour variation, which is crucial for neurological studies that compare reference areas such as rostrum, splenium, et cetera. We present quantitative and qualitative results that show that the method produces accurate, robust and rapid segmentations in a cross sectional study of 17 subjects, establishing its feasibility as a fully automated clinical tool for analysis and segmentation.
This paper presents notable improvements in the ability to control and distinguish the composite stress components within plasma enhanced chemical vapour deposition (PECVD) silicon nitride. Wafer curvature measurements complemented by stress structure fabrication and characterisation has enabled detailed analysis of in- and out-of-plane stress. Analytical modelling has allowed clarification of the relative contribution to the wafer curvature attributed solely to the stress gradient, which is of the order of 10-5 microns. Therefore the measured wafer curvature (due to composite stress), can be thought as a true representation of the actual wafer curvature due solely to the in-plane stress of the deposited thin film. This work represents a considerable advance compared with our previously published stress characterisation work on PECVD silicon nitride, which relied solely on wafer curvature measurements. However, the fabricated ring-beam and fixed-fixed structures were unable to resolve the in-plane stress component in high out-of-plane stress regimes. As predicted, at the zero stress gradient point, the fixed-fixed structures did measure an in-plane longitudinal compressive stress of -50MPa, which agrees well with wafer curvature measurements. Both stress components may now be repeatably controlled to realise tensile or compressive stresses (in-plane longitudinal) and positive or negative stress gradients (out-of-plane), by varying the RF deposition power. This new methodology allows for optimisation of the material for specific applications and in addition enhances the accuracy of micromechanical device models.
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