Using Kriging to Interpolate Spatially Distributed Volumetric Medical Data

Routine cases in diagnostic radiology require the interpolation of volumetric medical imaging data sets. Inaccurate renditions of interpolated volumes can lead to the misdiagnosis of a patient\u27s condition. It is therefore essential that interpolated modality space estimates accurately portray patient space. Kriging is investigated in this research to interpolate medical imaging volumes. Kriging requires data to be spatially distributed. Therefore, magnetic resonance imaging (MRI) data is shown to exhibit spatially regionalized characteristics such that it can be modeled using regionalized variables and subsequently be interpolated using kriging. A comprehensive, automated, three-dimensional structural analysis of the MRI data is accomplished to derive a mathematical model of spatial variation about each interpolated point. Kriging uses these models to compute estimates of minimal estimation variance. Estimation accuracy of the kriged, interpolated MRI volume is demonstrated to exceed that achieved using trilinear interpolation if the derived model of spatial variation sufficiently represents the regionalized neighborhoods about each interpolated voxel. Models of spatial variation that assume an ellipsoid extent with orthogonal axes of continuity are demonstrated to insufficiently characterize modality space MRI data. Model accuracy is concluded to be critical to achieve estimation accuracies that exceed those of trilinear interpolation

Hue-saturation-value feature analysis for robust ground moving target tracking in color aerial video

ABSTRACT Ground moving target tracking in aerial video presents a difficult algorithmic challenge due to sensor platform motion, non-uniform scene illumination, and other extended operating conditions. Theoretically, trackers which operate on color video should have improved performance vs. monochromatic trackers by leveraging the additional intensity channels. In this work, ground moving targets in color video are characterized in the Hue-Saturation-Value (HSV) color space. Using segmented real aerial video, HSV statistics are measured for multiple vehicle and background types and evaluated for separability and invariance to illumination change, obscuration, and aspect change. HSV statistics are then calculated for moving targets from the same video segmented with existing color tracking algorithms to determine HSV feature robustness to noisy segmentation