Solid tumors are characterized by abnormal blood vessel organization, structure, and function. These abnormalities give rise to enhanced vascular permeability and may predict therapeutic responses. The permeability and architecture of the microvasculature in human osteosarcoma tumors growing in dorsal window chambers in athymic mice were measured by confocal laser scanning microscopy (CLSM) and dynamic contrast enhanced magnetic resonance imaging (DCE-MRI). Dextran (40 kDa) and Gadomer were used as molecular tracers for CLSM and DCE-MRI, respectively. A significant correlation was found between permeability indicators. The extravasation rate Ki as measured by CLSM correlated positively with DCE-MRI parameters, such as the volume transfer constant Ktrans and the initial slope of the contrast agent concentration-time curve. This demonstrates that these two techniques give complementary information. Extravasation was further related to microvascular structure and was found to correlate with the fractal dimension and vascular density. The structural parameter values that were obtained from CLSM images were higher for abnormal tumor vasculature than for normal vessels.
Spectroscopic and polarimetric imaging have an increasing range of applications in remote sensing as well as inspection systems. It is shown how a limited polarimetric imaging capability can be added to a conventional hyperspectral camera based on a transmission grating imaging spectrometer. This is done by utilizing the undiffracted part of the light and separating its focus at the detector into two components using a simple walkoff plate composite. The resulting camera has full hyperspectral capability in the visible and near infrared spectral range, and in addition it forms broadband images for two orthogonal linear polarizations. Example imaging results are given and it is shown how polarimetric information can be used to detect manmade objects in a natural scene. A discussion of the limitations of the system is given.
Hyperspectral imaging has potential for detection of low-contrast targets in the presence of significant background clutter. We consider here the important case of detecting small targets as anomalies in a spatially cluttered natural background. In order to achieve a low false alarm rate, the properties of the background must be captured by the analysis procedure in sufficient detail to represent the full range of natural variation. Here we examine a statistical background model where background variations are represented by a sum of several multivariate normal probability distributions. The parameters of the statistical model are estimated using the stochastic expectation maximization (SEM) method. The quality of the resulting model's representation of natural backgrounds is discussed in terms of detection performance as a function of model complexity. Results are given for various illumination conditions and targets with different contrast to the background. We show that detection performance can be drastically improved by using multi-component background models, and that a low number of components is sufficient for detection of quite low contrast targets. The study is based on data with high spectral and spatial resolution from the Airborne Spectral Imager (ASI) hyperspectral sensor.
KEYWORDS: Target detection, Atmospheric modeling, Performance modeling, Detection and tracking algorithms, RGB color model, Sensors, Signal to noise ratio, Hyperspectral imaging, Reflectivity, Data modeling
We study material identification in a forest scene under strongly varying illumination conditions, ranging from open sunlit conditions to shaded conditions between dense tree-lines. The algorithm used is a physical subspace model, where the pixel spectrum is modelled by a subspace of physically predicted radiance spectra. We show that a pure sunlight and skylight model is not sufficient to detect shaded targets. However, by expanding the model to also represent reflected light from the surrounding vegetation, the performance of the algorithm is improved significantly. We also show that a model based on a standardized set of simulated conditions gives results equivalent to those obtained from a model based on measured ground truth spectra. Detection performance is characterized as a function of subspace dimensionality, and we find an optimum at around four dimensions. This result is consistent with what is expected from the signal-to-noise ratio in the data set. The imagery used was recorded using a new hyperspectral sensor, the Airborne Spectral Imager (ASI). The present data were obtained using the visible and near-infrared module of ASI, covering the 0.4-1.0 μm region with 160 bands. The spatial resolution is about 0.2 mrad so that the studied targets are resolved into pure pixels.
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