This work is concerned with the signal-to-symbol problem of building skinned, segmented, landmarked and labeled 3D models of the whole human body from range data. A fully automated model-based process is presented that takes raw range data, cleans and skins it, and then locates "interesting" features, to enrich the surface with symbolic information for specific applications. The method is validated via volumetrics in medicine and surface anthropometry.

Measurement of body volume (BV) can be used to estimate body composition using two‐ or multicomponent models. The traditional approach, underwater weighing (UWW), is awkward and unsuitable for many subjects. A newer alternative, whole body air displacement plethysmography (ADP), is less demanding but still unsuitable for young children, who may not remain still during the measurement. We have, therefore, considered whether a novel approach, three‐dimensional photonic scanning, is a viable alternative. Duplicate measurements of body volume were obtained in 22 adults (11 of each sex; mean [SD] BMI, 21.8 [2.5] kg/m2) by UWW, ADP, and a Hamamatsu Bodyline Scanner (HBS) (Hamamatsu, Japan). Subjects wore a tight‐fitting swimming costume for all three measurements, which were performed within one day of each other. Scans lasted 10 seconds, with the subject standing in a predefined position. The body surface skin was reconstructed using a B‐spline‐fitting model. In UWW, lung volume (LV) was measured simultaneously with underwater weight. In ADP and HBS, LV was predicted from weight and height. Results were compared using correlation and Bland and Altman analysis. Correlation analysis indicated that the scanner successfully ranked subjects in terms of BV. However, Bland and Altman analysis demonstrated that, relative to both UWW and ADP, HBS measured BV without bias but with limits of agreement between individuals of >2 liters, equivalent to approximately 20% fat. Scan precision was 0.57 liters, or 4.1% fat. Although HBS cannot yet measure BV with sufficient accuracy to predict fatness, much of the error is probably due to difficulties in standardizing LV during the scan. Simultaneous measurement of LV with volume by HBS is expected to improve limits of agreement substantially. Occlusion is also an important source of error. The method offers many advantages over alternative techniques, because the measurement is brief, noninvasive, and suitable for repeat measurements.

No abstract

Pretreatment ischemic location may be an important determinant for functional outcome prediction in acute ischemic stroke. In total, 143 anterior circulation ischemic stroke patients in the THRACE study were included. Ischemic lesions were semi-automatically segmented on pretreatment diffusion-weighted imaging and registered on brain atlases. The percentage of ischemic tissue in each atlas-segmented region was calculated. Statistical models with logistic regression and support vector machine were built to analyze the predictors of functional outcome. The investigated parameters included: age, baseline National Institutes of Health Stroke Scale score, and lesional volume (three-parameter model), together with the ischemic percentage in each atlas-segmented region (four-parameter model). The support vector machine with radial basis functions outperformed logistic regression in prediction accuracy. The support vector machine three-parameter model demonstrated an area under the curve of 0.77, while the four-parameter model achieved a higher area under the curve (0.82). Regions with marked impacts on outcome prediction were the uncinate fasciculus, postcentral gyrus, putamen, middle occipital gyrus, supramarginal gyrus, and posterior corona radiata in the left hemisphere; and the uncinate fasciculus, paracentral lobule, temporal pole, hippocampus, inferior occipital gyrus, middle temporal gyrus, pallidum, and anterior limb of the internal capsule in the right hemisphere. In conclusion, pretreatment ischemic location provided significant prognostic information for functional outcome in ischemic stroke.