KEYWORDS: Target detection, Signal to noise ratio, Detection and tracking algorithms, Data conversion, Single mode fibers, Hyperspectral imaging, Image segmentation, Hyperspectral target detection, Algorithms, Sensors
Irregular illumination across a hyperspectral image makes it difficult to detect targets in shadows, perform change
detection, and segment the contents of the scene. To correct for the data in shadow, we first convert the data from
Cartesian space to a hyperspherical coordinate system. Each N-dimensional spectral vector is converted to N-1 spectral
angles and a magnitude representing the illumination value of the spectra. Similar materials will have similar angles and
the differences in illumination will be described mostly by the magnitude.
In the data analyzed, we found that the distribution of illumination values is well approximated by the sum of two-
Gaussian distributions, one for shadow and one for non-shadow. The Levenberg-Marquardt algorithm is used to fit the
empirical illumination distribution to the theoretical Gaussian sum. The LM algorithm is an iterative technique that
locates the minimum of a multivariate function that is expressed as the sum of squares of non-linear real-valued
functions.
Once the shadow and non-shadow distributions have been modeled, we find the optimal point to be one standard
deviation out on the shadow distribution, allowing for the selection of about 84% of the shadows. This point is then used
as a threshold to decide if the pixel is shadow or not. Corrections are made to the shadow regions and a spectral matched
filter is applied to the image to test target detection in shadow regions. Results show a signal-to-noise gain over other
illumination suppression techniques.
Designing and testing algorithms to process hyperspectral imagery is a difficult process due to the sheer volume of the
data that needs to be analyzed. It is not only time-consuming and memory-intensive, but also consumes a great amount
of disk space and is difficult to track the results. We present a system that addresses these issues by storing all
information in a centralized database, routing the processing of the data to compute servers, and presenting an intuitive
interface for running experiments on multiple images with varying parameters.
KEYWORDS: Magnetic resonance imaging, Data modeling, Tissues, Head, Optical filters, Medical imaging, Software development, Matrices, Data processing, Surgery
A method for fully automating the measurement of various neurological structures in MRI is presented. This technique uses an atlas-based trained maximum likelihood classifier. The classifier requires a map of prior probabilities, which is obtained by registering a large number of previously classified data sets to the atlas and calculating the resulting probability that each represented tissue type or structure will appear at each voxel in the data set. Classification is then carried out using the standard maximum likelihood discriminant function, assuming normal statistics. The results of this classification process can be used in three ways, depending on the type of structure that is being detected or measured. In the most straightforward case, measurement of a normal neural sub-structure such as the hippocampus, the results of the classifier provide a localization point for the initiation of a deformable template model, which is then optimized with respect to the original data. The detection and measurement of abnormal structures, such as white matter lesions in multiple sclerosis patients, requires a slightly different approach. Lesions are detected through the application of a spectral matched filter to areas identified by the classifier as white matter. Finally, detection of unknown abnormalities can be accomplished through anomaly detection.
KEYWORDS: Video coding, Video, Image segmentation, Video compression, Data modeling, 3D modeling, Computer programming, Motion estimation, Quantization, Mathematical modeling
An image intensifier-based rotational volume tomographic angiography imaging system has been constructed. The system consists of an x-ray tube and an image intensifier that are separately mounted on a gantry. This system uses an image intensifier coupled to a TV camera as a 2D detector so that a set of 2D projections can be acquired for a direct 3D reconstruction. Although an image intensifier offers good detection quantum efficiency and possibly results in a better low contrast resolution than a fluorescent screen, it introduces two types of distortion: S distortion and pincushion distortion. To obtain accurate reconstructions, these distortions must be corrected prior to 3D reconstruction. Techniques for the correction of these distortions have been developed. These techniques were tested using experimental data acquired with the image intensifier-based volume tomographic angiography imaging system. The results indicate that the distortion correction techniques work well.
This paper presents a quick and efficient way to detect and correct the linear and constant image-phase terms associated with MR images. We show that this correction provides us with the knowledge of the exact location of the DC term in k-space, which proves to be useful in the detection of x and y motion parameters. In addition, by displaying the real positive part of the image after the proposed correction, we can reduce background noise, motion artifacts and flow artifacts. Examples, analyses and results are provided to demonstrate the usefulness of the proposed detection and correction method.
KEYWORDS: Motion models, Data modeling, Magnetic resonance imaging, Data acquisition, 3D modeling, Image processing, Mathematical modeling, 3D image processing, Fourier transforms, Computer programming
In this paper, we present a comprehensive model for MR data acquisition in the presence of patient motion to provide a better understanding as to the source of motion artifacts. This model identifies and quantifies various sources of motion artifacts in 2-D Fourier imaging. We verify our model by comparing the results predicted by the model with actual MR images of phantoms subjected to motion with controlled parameters. We expect that the knowledge of the sources of artifacts will lead to new and better methods of compensating for them.
Artifacts due to patient motion in the slice-selection direction (Z motion) have been a major source of MR image degradation for many years but have not been addressed as much as in-plane motion due to the complexity of modeling and correcting for the motion. In this paper we present a model and a detection and correction scheme for amplitude aberrations due to motion in the slice-selection direction. 1.
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