Abstract: In CT (computed tomography) images, streak artifacts caused by the presence of metal implants and inhomogeneous estimation of tissue density are reduced or eliminated. The algorithm has two basic steps: 1) illumination correction and 2) adaptive 3D filtering. The algorithm starts by estimating the direction of the streak and the degree of inhomogeneous densities by gray scale morphology dilatation. Then, it proceeds to estimate the correct densities based on the estimations and to reduce the streak by an adaptive 3D filtering whose parameters depend on the streak direction and the local image contrast.

Abstract: Structures are delineated in medical or other images. First, various tissue types present in the image are statistically described using a maximum likelihood classifier. Second, the tissue of interest is described using an exemplar, which is derived either from an anatomical atlas or from user input. Third, the structure of interest is morphologically described. The process can be iterated until a desired level of accuracy is achieved.

Abstract: In a sequence of medical image data showing tumors and blood vessels, a plasma signal is optimized to avoid flow artifacts by receiving a user input of a blood region and using the user input to seed an automated search. Each voxel is scored by time point of maximum intake, slope at maximum intake, peak value and conformance to a gamma variate curve, and the voxels with the highest scores are included in the ideal plasma region of interest. Uptake curves for both tumors and plasma are determined and used to estimate a volume transfer constant.

Abstract: Abnormal regions in volumetric image sets are detected and delineated through the following technique. Noise is suppressed in the original data. The background is classified into one or more background classes. An exemplar is identified. Essentially similar structures throughout the volume are identified; a directed clustering technique has been developed for doing so. Quantitative information (lesion volume, shape, etc.) is extracted and output to database.

Abstract: MRI scans typically have higher resolution within a slice than between slices. To improve the resolution, two MRI scans are taken in different, preferably orthogonal, directions. The scans are registered by maximizing a correlation between their gradients and then fused to form a high-resolution image. Multiple receiving coils can be used. When the images are multispectral, the number of spectral bands is reduced by transformation of the spectral bands in order of image contrast and using the transformed spectral bands with the highest contrast.

Abstract: MRI scans typically have higher resolution within a slice than between slices. To improve the resolution, two MRI scans are taken in different, preferably orthogonal, directions. The scans are registered by maximizing a correlation between their gradients and then fused to form a high-resolution image. Multiple receiving coils can be used. When the images are multispectral, the number of spectral bands is reduced by transformation of the spectral bands in order of image contrast and using the transformed spectral bands with the highest contrast.

Abstract: An automated process classifies MRI or other medical imaging data as belonging to neurological structures. A training part of the process uses a baseline data set with accompanying classification map, as well as an arbitrary number of training data sets, also with accompanying classification maps. Each training data set is registered to the baseline data set using a warp-based automated registration algorithm. The resulting probability map, mean values and covariance matrices are used to classify structures in an image data set using a maximum likelihood criterion.

Abstract: In CT (computed tomography) images, streak artifacts caused by the presence of metal implants and inhomogeneous estimation of tissue density are reduced or eliminated. The algorithm has two basic steps: 1) illumination correction and 2) adaptive 3D filtering. The algorithm starts by estimating the direction of the streak and the degree of inhomogeneous densities by gray scale morphology dilation. Then, it proceeds to estimate the correct densities based on the estimations and to reduce the streak by an adaptive 3D filtering whose parameters depend on the streak direction and the local image contrast.

Abstract: Abnormal regions in volumetric image sets are detected and delineated through the following technique. Noise is suppressed in the original data. The background is classified into one or more background classes. An exemplar is identified. Essentially similar structures throughout the volume are identified; a directed clustering technique has been developed for doing so. Quantitative information (lesion volume, shape, etc.) is extracted and output to database.

Abstract: Abnormal regions in volumetric image sets are detected and delineated through the following technique. Noise is suppressed in the original data. The background is classified into one or more background classes. An exemplar is identified. Essentially similar structures throughout the volume are identified; a directed clustering technique has been developed for doing so. Quantitative information (lesion volume, shape, etc.) is extracted and output to database.

Abstract: Abnormal regions in volumetric image sets are detected and delineated through the following technique. Noise is suppressed in the original data. The background is classified into one or more background classes. An exemplar is identified. Essentially similar structures throughout the volume are identified; a directed clustering technique has been developed for doing so. Quantitative information (lesion volume, shape, etc.) is extracted and output to database.

Abstract: In a human or animal organ or other region of interest, specific objects, such as liver metastases and brain lesions, serve as indicators, or biomarkers, of disease. In a three-dimensional image of the organ, the biomarkers are identified and quantified both before and after a stimulus is applied, and their reaction to the stimulus is observed. Statistical segmentation techniques are used to identify the biomarker in a first image and to carry the identification over to the remaining images.

Abstract: In a human or animal brain or other nerve tissue, specific objects or conditions, such as brain lesions and plaques, serve as indicators, or biomarkers, of neurological disease. In a three-dimensional image of the region of interest, the biomarkers are identified and quantified. Multiple three-dimensional images can be taken over time, in which the biomarkers can be tracked over time. Statistical segmentation techniques are used to identify the biomarker in a first image and to carry the identification over to the remaining images.

Abstract: In a human or animal joint, specific objects serve as indicators, or biomarkers, of joint disease. In a three-dimensional image of the joint, the biomarkers are identified and quantified. Multiple three-dimensional images can be taken over time, in which the biomarkers can be tracked over time. Statistical segmentation techniques are used to identify the biomarker in a first image and to carry the identification over to the remaining images.