Improvised explosive devices (IEDs) are common and lethal instruments of terrorism, and linking a terrorist entity to a
specific device remains a difficult task. In the effort to identify persons associated with a given IED, we have
implemented a specialized content based image retrieval system to search and classify IED imagery. The system makes
two contributions to the art. First, we introduce a shape-based matching technique exploiting shape, color, and texture
(wavelet) information, based on novel vector field convolution active contours and a novel active contour initialization
method which treats coarse segmentation as an inverse problem. Second, we introduce a unique graph theoretic approach
to match annotated printed circuit board images for which no schematic or connectivity information is available. The
shape-based image retrieval method, in conjunction with the graph theoretic tool, provides an efficacious system for
matching IED images. For circuit imagery, the basic retrieval mechanism has a precision of 82.1% and the graph based
method has a precision of 98.1%. As of the fall of 2007, the working system has processed over 400,000 case images.
KEYWORDS: Data modeling, In vivo imaging, Heart, 3D modeling, Image segmentation, 3D image processing, Cardiac imaging, Image processing, Transducers, Computer simulations
Active contours have been used in a wide variety of image processing applications due to their ability to effectively distinguish image boundaries with limited user input. In this paper, we consider 3D gradient vector field (GVF) active surfaces and their application in the determination of the volume of the mouse heart left ventricle. The accuracy and efficacy of a 3D active surface is strongly dependent upon the selection of several parameters, corresponding to the tension and rigidity of the active surface and the weight of the GVF. However, selection of these parameters is often subjective and iterative. We observe that the volume of the cardiac muscle is, to a good approximation, conserved through the cardiac cycle. Therefore, we propose using the degree of conservation of heart muscle volume as a metric for assessing optimality of a particular set of active surface parameters. A synthetic dataset consisting of nested ellipsoids of known volume was constructed. The outer ellipsoid contracted over time to imitate a heart cycle, and the inner ellipsoid compensated to maintain constant volume. The segmentation algorithm was also investigated in vivo using B-mode data sets obtained by scanning the hearts of three separate mice. Active surfaces were initialized using a broad range of values for each of the parameters under consideration. Conservation of volume was a useful predictor of the efficacy of the model for the range of values tested for the GVF weighting parameter, though it was less effective at predicting the efficacy of the active surface tension and rigidity parameters.
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