Initial results of multilevel principal components analysis of facial shape

Traditionally, active shape models (ASMs) do not make a distinction between groups in the subject population and they rely on methods such as (single-level) principal components analysis (PCA). Multilevel principal components analysis (PCA) allows one to model between-group effects and within-group effects explicitly. Three dimensional (3D) laser scans were taken from 240 subjects (38 Croatian female, 35 Croatian male, 40 English female, 40 English male, 23 Welsh female, 27 Welsh male, 23 Finnish female, and 24 Finnish male) and 21 landmark points were created subsequently for each scan. After Procrustes transformation, eigenvalues from mPCA and from single-level PCA based on these points were examined. mPCA indicated that the first two eigenvalues of largest magnitude related to within-groups components, but that the next largest eigenvalue related to between-groups components. Eigenvalues from single-level PCA always had a larger magnitude than either within-group or between-group eigenvectors at equivalent eigenvalue number. An examination of the first mode of variation indicated possible mixing of between-group and within-group effects in single-level PCA. Component scores for mPCA indicated clustering with country and gender for the between-groups components (as ex-pected), but not for the within-group terms (also as expected). Clustering of component scores for single-level PCA was harder to resolve. In conclusion, mPCA is viable method of forming shape models that offers distinct advantages over single-level PCA when groups occur naturally in the subject population

Cerebellar Morphometry and Cognition in the Context of Chronic Alcohol Consumption and Cigarette Smoking.

BackgroundCerebellar atrophy (especially involving the superior-anterior cerebellar vermis) is among the most salient and clinically significant effects of chronic hazardous alcohol consumption on brain structure. Smaller cerebellar volumes are also associated with chronic cigarette smoking. The present study investigated effects of both chronic alcohol consumption and cigarette smoking on cerebellar structure and its relation to performance on select cognitive/behavioral tasks.MethodsUsing T1-weighted Magnetic Resonance Images (MRIs), the Cerebellar Analysis Tool Kit segmented the cerebellum into bilateral hemispheres and 3 vermis parcels from 4 participant groups: smoking (s) and nonsmoking (ns) abstinent alcohol-dependent treatment seekers (ALC) and controls (CON) (i.e., sALC, nsALC, sCON, and nsCON). Cognitive and behavioral data were also obtained.ResultsWe found detrimental effects of chronic drinking on all cerebellar structural measures in ALC participants, with largest reductions seen in vermis areas. Furthermore, both smoking groups had smaller volumes of cerebellar hemispheres but not vermis areas compared to their nonsmoking counterparts. In exploratory analyses, smaller cerebellar volumes were related to lower measures of intelligence. In sCON, but not sALC, greater smoking severity was related to smaller cerebellar volume and smaller superior-anterior vermis area. In sALC, greater abstinence duration was associated with larger cerebellar and superior-anterior vermis areas, suggesting some recovery with abstinence.ConclusionsOur results show that both smoking and alcohol status are associated with smaller cerebellar structural measurements, with vermal areas more vulnerable to chronic alcohol consumption and less affected by chronic smoking. These morphometric cerebellar deficits were also associated with lower intelligence and related to duration of abstinence in sALC only

What’s in a Smile? Initial results of multilevel principal components analysis of facial shape and image texture

Multilevel principal components analysis (mPCA) has previously been shown to provide a simple and straightforward method of forming point distribution models that can be used in (active) shape models. Here we extend the mPCA approach to model image texture as well as shape. As a test case, we consider a set of (2D frontal) facial images from a group of 80 Finnish subjects (34 male; 46 female) with two different facial expressions (smiling and neutral) per subject. Shape (in terms of landmark points) and image texture are considered separately in this initial analysis. Three-level models are constructed that contain levels for biological sex, “within-subject” variation (i.e., facial expression), and “between-subject” variation (i.e., all other sources of variation). By considering eigenvalues, we find that the order of importance as sources of variation for facial shape is: facial expression (47.5%), between-subject variations (45.1%), and then biological sex (7.4%). By contrast, the order for image texture is: between-subject variations (55.5%), facial expression (37.1%), and then biological sex (7.4%). The major modes for the facial expression level of the mPCA models clearly reflect changes in increased mouth size and increased prominence of cheeks during smiling for both shape and texture. Even subtle effects such as changes to eyes and nose shape during smile are seen clearly. The major mode for the biological sex level of the mPCA models similarly relates clearly to changes between male and female. Model fits yield “scores” for each principal component that show strong clustering for both shape and texture by biological sex and facial expression at appropriate levels of the model. We conclude that mPCA correctly decomposes sources of variation due to biological sex and facial expression (etc.) and that it provides a reliable method of forming models of both shape and image texture

Prehistoric Settlement, Mobility and Societal Structure in the Peak District National Park: New Evidence from Ceramic Compositional Analysis

Detailed compositional and technological analysis of a large assemblage of prehistoric ceramics from numerous sites situated within the Peak District National Park has been used to explore the settlement patterns, societal structure, mobility and interaction of the populations that inhabited this area during the Early Bronze Age to Early Iron Age. A surprising pattern emerges of the widespread dominance of a single, geographically restricted temper type, which appears to have been transported and mixed with locally procured clay and used to produce pottery at numerous different sites. The distribution of this and several other compositional groups are defined via thin-section petrography and compared to raw material field samples. The resulting patterns are used to assess the validity of previous theories about prehistoric life in this region during the third to first millennia bc

Customized design of hearing aids using statistical shape learning

3D shape modeling is a crucial component of rapid prototyping systems that customize shapes of implants and prosthetic devices to a patient’s anatomy. In this paper, we present a solution to the problem of customized 3D shape modeling using a statistical shape analysis framework. We design a novel method to learn the relationship between two classes of shapes, which are related by certain operations or transformation. The two associated shape classes are represented in a lower dimensional manifold, and the reduced set of parameters obtained in this subspace is utilized in an estimation, which is exemplified by a multivariate regression in this paper.We demonstrate our method with a felicitous application to estimation of customized hearing aid devices